Analysing online bookstore sales.ipynb

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P9GmfS37fv7HMfwY8QR+K/Cn/ABMPAviH/SLK4i+ZYd38BP8AnrXzRX2J+wV+1TpniHwxcfCP4g4uvDetAxWc8p/48pSMKQfTIFeL/tgfssap+y/8TLjTZ1afSp2MljdqMxzxnlSD06Yp16cZR9vSWj3XZ/5djmybH1qNV5Xj3+8j8Mv549/VdfvPI6KKK4z6gKKKKACiiigAooooAK+of+Cef7Ytp8JdXuvA3jVf7R+H3isfZryGb5ltCcYkXPTp29a+Xq6b4OfDu++LHxP0Tw9psTTXeqXaQoqj3yf0BrbD1J06ilDf+tDzc3weHxWEnSxPw2vftbW6fRrc9Y/b6/Y+l/Za+JCT6bL9u8H69m40a+X7s0ZyQPqBXu//AASI/wCCm1z+zz4ug8H+Kr2SXwzfuFiklbP2Vicd+3P6VzP/AAWE8e2uieMPCfwo0ucXGm/DrTktmYHJE/zBgT9MV8Z26yNOgiDGQn5QvXPtXbUqvCYtzw+ln/w69D5zCZfHPMhhRzNX51o+tvsy8m1Zs/qj8OeIrTxXoltqFjMlxa3UYkjdDkMDV6vkH/gjboXjvR/2X7JvGE0jwyqDYxyg70j5xmvr6v1DB4h16EarVrrY/krOMBHBY2phYzU1BtXXUKKKK6TzQr5x/wCCs/8Ayj++JH/YJm/9FtX0dXzj/wAFZ/8AlH98SP8AsEzf+i2qKnwsqHxI/lIvP+QjL/10P86/qI/4IX/8o0/h5/15r/6Ctfy73n/IRl/66H+df1Ef8EL/APlGn8PP+vNf/QVrzMB8bO/F/Cj69ooor1jzgooooAKbJKsKFmIVRySTgCmXl2lhaSTSsEjiUuzE8ACvxX/4LN/8F3dX0Pxfqnw3+GN19lWzZoL3UY25Y5IIU/h+tZ1asaavIunTc3ZH6g/H/wD4KGfCb9mq0kk8T+LtMtXi+9EsmXH4V8afFj/g55+EHgq5lj0aw1HXguQrwKuG/wDHhX4/fs1fsW/GX/gpF41kn01dR1COWQ+fqV2SYkJP1Ge/T0r9IPgN/wAGrdimnwXPjbxW7XWAZILcYTP5GuRVq1TWC0Or2VKHxsbrn/B1tpzzH7B4LvQmePMx0/76r85P+CoH7fX/AA8J+Ndl4s/s1tM+y2v2fy278L7/AOzX7NaD/wAG0/wO0+JBdR3dyy/eO5fm/wDHa/JX/gtX+x94T/Yv/actfDPg+B7fTpbTzSrEZzhPYeprHEKtyXm9DSi6XN7m59H/APBq3/ycz4s/7Bo/9GR1++1fgT/wat/8nM+LP+waP/RkdfvtXXg/4RzYr+IIelfybf8ABVX/AJSGfFT/ALDH/tKOv6yT0r+Tb/gqr/ykM+Kn/YY/9pR1lj/hRpg/iZ+rv/Bqj/yQTxV/1/v/AEr9ba/JL/g1R/5IJ4q/6/3/AKV+ttdGG/hIwr/xGFFFIzbFJPAHJPpW5kfzC/8ABeLw3HoH7fPiBkUL9q/eHHc4H+NfSn/BqdrbR/tCeNrDJ2Npiy47Z8xBXyv/AMFvfG8fjP8Ab68VeU28WUvkkg/Qf0r68/4NSPB0k/xX8c63sPlR2i2pbHfdG39a8in/ALwelP8AgH7p1S13xHY+GLB7rULuCzt4xlpJW2gCuU/aL+Pehfs0/CLWPF/iG5jttP0mB5TubHmMASEHuSMfjX84/wDwUP8A+CzfxI/bT8cXdjpF/eaL4ZErR21nbMQ0q5wCeucj+dehWrxprXc46VFz2P2+/aE/4LPfAz9nuSaC/wDFdnd3sWQIbdgxJ9K+T/iJ/wAHUXw90W4kh0bwxq2obT8soVdp/wDH6+A/2M/+CF3xe/bEt7fW9SD6FpF3iTzr0HzJVPcDOf0r9APhR/wa2eAdAsYz4i8QXepzkfOBgAH/AL5rnVTET1irGzhRhu7nmuq/8HV0NwJUh8G3AjdSo3YzyP8Aer8cfHfiT/hMPGeqars2f2jdSXG3+7uYnH61/RLF/wAG3nwF03T5mlsbqZljJBLL1A/3a/nY8baZHovjPV7OEYhtL2aFB6KrsB+grmxKqpL2hvQdN35D+lX/AIN3/wDlGT4Y/wCv64/9Bjr7iIyK+Hf+Dd//AJRk+GP+v64/9Bjr7jr06P8ADR59T42fz4/8HHn7Bmq/Cn9oq4+JWl2MkugeKX82eSNflt5cYwfwUfnWH/wQJ/4KmaV+xX411Hwh4xuGh8LeIHVopWPy2kuTk/jkflX7Z/8ABRLxd8MPCn7Netv8VPscmgyQMohlI8yRuwQetfzGXfwSk/aV/aN1TR/hBoWoXunXFyzWNuBlootxxuOABxXBXi6dXnh1O2k1Up8sj+q/wN+0x4E+I2kQXukeJtKu4LhA6lZh0NZHxo/bK+HXwH8LXWq6/wCJ9MtorZC+zzQWfHYV/Ncv7F37UHwfnNna6H4qsAhwBBJlf0zVvTf+CfP7Tv7QmqQ21/ofiO7R2wZLuQBU9zkitfrc9lHUz+rR/mHf8Fff27h+3t+1Pd6xpfmHRLIC0sEH8aqSAce4xX6uf8G1H7EGofs//s+6v438Q2b2ureL5Ue0SRcPHbgEf+PYU15r/wAE2v8Ag2+g8Ba5p/iv4rXEOoXNu6zxacg+QMDkbuv86/XnQ9EtfDekW9jZQpb2trGsUUaDARVGAPyFPD0Zc3tKm4q1WPL7OBNeP5dpKR1CEj8q/lm/4LVa1Nrn/BQ3xtLOxZllEYyewZ8V/U3PH5sDr/eUiv5e/wDguv4Jl8Gf8FE/FysjCO52yoxH3ss5ox3wIMJ8R5T+yV41+Nnw/h1C4+FC62iXLD7U9jCHyR0zmva/+Gj/ANtP/nt41/8AAVP8K+0P+DU/xfY3+keOdDuUgeeOaOSNXUEkbecZ+tfsx/Ydl/z52v8A36X/AArKjhuaCkpF1a/LK1j+Zf8A4aP/AG0/+e3jX/wFT/Cj/ho/9tP/AJ7eNf8AwFT/AAr+mj+w7L/nztf+/S/4Uf2HZf8APna/9+l/wrb6m/5mZ/WV/KfymfHXwf8AtH/tKXlrceNNC8U63NZLthaW2AKDn0+pr9Cv+DZD9m/x38LPjD4y1LxB4f1DRrCW2hQNcx7fMYeZ0/Ov2p/sOy/587X/AL9L/hUtvYw2efKhiiz12IFz+VOnhOWfPcU8S5R5bEtFFFdhzBRRRQAUUUUAFFFFABRRRQAUUUUAFFFFAHE/tAfAjQf2jfhhqXhfxDapc2V/EUyQC0TEEBlPYjNfz2/t0fsa67+xr8Zb3Q9Rhd9MlkaTT7rads0Z5HPqM4/Cv6SK8F/b/wD2J9D/AGzPgvfaTeQImrwIZLC7Vf3kMgBxz6cnivAz3KFi6ftKfxr8fI/RfD7jSeS4v2Nd3oTeq7P+Zfr5H85NtcvaXCSxsUkjYMrA4KkdK+7v2Zfipov7fPwX/wCFS+N544vFVhFnQdRlIy5AwIyx/wCA96+O/jf8F9c+AfxG1Hw1r9pJa3thKUO4cOB0I9qwPDXiW+8H65balptzLaXto4kiljbDKwORX53RqujNqS02aP6bzPL6WZYaNSjK0l70Jro+j9H1XVG18ZfhJq/wR+Ieo+HdatpLa8sJSmHUjevYj2Iwfxrlq/QS7t9G/wCCq37OXnWyQ2fxg8GWvzQoAG1iJeOPU8r+VfA2u6HdeGtXuLG9hkt7q1cxyxuMMhHrTxND2bUoO8Xs/wCuq6iyTNZYqEqOIXLWp6Tj+q7xe6ZUooorlPdCiiigAooooAK+6f8Agm18PNP/AGZ/hL4i+Pvi+JEi063aDw9DIPmubg4G5M9x8/T0rwP9h79ka+/ap+KcNrLm08N6eRPqt8/EcEQ5Iz6kZ/Ku4/4KPftW2fxK1yw+H/hEJaeBvBai1tUh4F3IoO6RvfJavQwqVGP1mX/bvm+/ovzPkc8qSzCssmw70etV9ofy+stvS7Pnb4lePtQ+KXjzVfEGqTNPf6tcNcTSMclmNfWf/BL39iaw8feK7Tx38QZrfR/BmnTKInvHEa3cmeFXdgHofzrzj9jj9ke3+IEE/jnxzOdE+H2gnzZrmXg37ryIY/Uk7Qen3qp/tgftq33x31a30fQIv7A8F6H+502wtjsBVcAO2OSxAB5J61NFRp2r1lfsu/m/L8x5lUrYy+VZa+VJWnNbQX8se8vyW5/RN4RtrCz8NWUel+V/Z6QqIDFjYUxxjFaVfkV/wRs/4KeeKB4psPhr4kgvNcsLgiKzugNz23sx9OtfrnG+9AfUZr9NyzMKeMo+0p6W3XY/lTijhzFZLjXhcTrfVPuu46iiivRPmwr5x/4Kz/8AKP74kf8AYJm/9FtXs3xl+L2i/Aj4can4p8Q3K2mk6VEZZ5T2Ffkb/wAFGP8Ag4n+F3xv+APivwB4W0XXbu51q1ktFvJY0EAypXIIfPf0rGtUjGLTZrShKUro/FS8/wCQjL/10P8AOv6iP+CF/wDyjT+Hn/Xmv/oK1/LpPN5ty7/3mLfrX6F/sf8A/BxN8QP2PPgHofgDSfBHhzVLHQ4hFHc3F1KkkgAA5AGO1eXhasYSvI78RTc42R/R7RX4If8AEWL8Uv8AonHhH/wNn/wo/wCIsX4pf9E48I/+Bs/+Feh9cpdzj+q1D92/G3iJfCHgzV9WYbl0uymu2HqI0Z/6V+Cnjf8A4OT/AI0y/HDUtN01NEh0mPVJbW3U2ilhEsjKuT64AqHx7/wdM/E7x74F1rQ5fh94Vt4tasJ7B5UvZi0SyxshYDHUbs1+anhe+fU/iDaXMn+suLwSN9S2TXNXxV7Kmzoo4e1+dH9XP7SXxSv7L9gbXPE6nGozeHluSycbXZVJx+dfyialq8/j/wAetd38rPNql2DNIx5+ZgCc1/Wfe/Dr/hbX7EaeHOM6t4cigGfXy1P9K/lY/af+Bmq/s4fHDXvC2p201pPpd00abxjcoJAYflTx19H0FhLao/qG/wCCYvwW8P8Awb/Y88G22hWdvbrd6bBcTPGoBldo1YsSOvJP519CV+I//BJP/g4R8N/CH4T6b4A+KsdxbjS1WCz1RADGIl4AfJGMDHQHpX3L4n/4L7/s1eH9CN3D490/UpQu77NbHMp9sHA/WuqnWp8q1OadKfNsfaTuI0LE4AGST2r+bz/g4+8c6T40/bhQaXfW999htDDP5LhvLfCcHHfg/lXu/wC3p/wcl658cNMuPBvwV0S809NR/wBHOpTKPtThvlwigsOc9fevz1/al/Y5+J3wV8KaV41+IVtdQSeKybiM3TMZn3c5OfXNcuKrKceWB0Yek4vmkfcP/Bq3/wAnM+LP+waP/RkdfvtX8vX/AARx/wCCjegf8E6fjNqmveItKv8AU7HVLcW7C0UGRPmU5wWA7V++v7An/BUD4c/8FEbPVD4Klu47zRkSS7tLpVWWFXJCkgEjkqe/atcHOPJy31IxUJc3N0Po89K/k2/4Kq/8pDPip/2GP/aUdf1knpX8m3/BVX/lIZ8VP+wx/wC0o6nH/Ch4P4mfqd/waoeJ7D/hUfivS/tcH9oLdtKbfeN4U9Djrjg/lX6+V/Ij+yl+0T8Rv2QvFtt488FT3tpFbzBJWUZguACMxsORznHTvX7D/sz/APB0x4E8R+Hba2+Ivh3UdH1hECyTWQV7ZyBgnLMDk/SjDYiCioy0CvQk5c0T9Z68x/a//aB0j9mn4A+IfE+r3UdtFaWcvlbmwXk2HaB75xXw98R/+Dn74I+FtCkl0iw17WL3afKijij27u24784r8n/+Clf/AAV98ef8FDde+zXX/Eh8K275t9MtpDtbn7znqTwOM9q1q4uEV7ruzOnh5N67Hzp8f/ijcfGn4yeIfElwxd9VvpZlJ67S5K/oRX7/AP8Awbe/sqz/AAL/AGQz4ivoDDeeMJPthVxhlGAo/wDQK/JD/gkn/wAEyvEH7dPxz097ixuIPB+mTCW/u2TCOAR8mffn8q/pv+HXgWw+GfgnTdC02FILLTIFgiRBgAAVz4Kk2/aSNsVNJciPzC/4OsfiVqPhb9l/wRoNrLJHaeItTm+0hWwG8nyWUH8WNflP/wAEgfhFovxt/b38C6Dryxy6dc3qeZG4GH+ZeOfrX7V/8HGH7JWrftL/ALFS6joVs95qvgy4N+sKDJMJ2GU/giE1/PZ8CfjHrH7OXxe0nxTpJaDU9EuRKoPByp5B/KoxWlZSlsXh9aVluf2F+HtCtfDOi21jZQxwWtrGI40RcBQBV2vzi/ZS/wCDj34K/EzwJYL401L/AIQ/WoYVS5W74iZwMEqQScfWug+NX/Bxp+z58MtJll0jWpPFlwFPlxaaobJ7Z3FeK9D29O17nF7Gd7WPuD4leN9K+HXgXU9Y1u+t9N0yxt3knuJ3CJGuDySeK/jp+Ilyl78QNdmiYPHLqFw6MOjAyMQa++v2sf8Ago98cf8Ags58TbbwH4G0a+07w9cS7YtL09m/fDoHlbr35GSOa+H/ANoT4Iaz+zf8ZNe8E+IFRdY8Pzi3ugmSu8orcZ/3hXm4ur7SzitEd2Hp8m+5/Rt/wbv/APKMnwx/1/XH/oMdfcdfhb/wSL/4L0/Dn9jr9mjSPht4u0bW/NsrmSX7bbRo0WHCjnLj+76V+0fwM+Nmh/tCfDTTfFXh6f7RpmpxCWJj1wfWvQw84uCSZx1oSUm2fiR/wdL/ABu1vVfjx4c8IebNBo1jZ+d5IYhJXLON2OnT+VY3/BtN+0/8LPgJ8RfFln44vLDR9Z1eOBdOvrwqsa4Mm4b2xt6j619f/wDBxf8AsU+Dvjl8MbPxxc+K9H8M+JfDkJjRb2Tat7HydnAJ3ZY9q/ALTNJvrq+ZNPjnnkjPDW4JP14rgrOVOvznXSSnS5T+wzSviz4G8aRLNZ694f1JGGQ8VzHICPqDXQaLe6Xc/wDHhJaP/wBcSD/Kv4+bTx58QPDiBINX8U2arwFS7mQD8jVq1/aM+JeiyAx+MvGEBU8Y1O4H/s1a/X+8SPqfmf2G0V/Jj4A/4KefHb4XXkU2lfEPXkkhIK/aJPPHHs+c196/sHf8HOfjLQ/Fen6F8YLK11rSLl1jk1iECO4g7Z2KFTFawxsJOz0M5YWSV0fuzX4df8HRf7KVxpvjLRPiRY2xNrcr9mumVc/N8uCfzNftV8OviFpPxU8Fad4g0O8iv9L1SET288Zyrqa4b9sb9ljRP2v/AIF6z4O1qJGW+gYQSlcmGTB2kfjitq1P2kLGVKfJO7P5v/8AgjV+24n7Ff7XGl6jqMpj0DVyLS+5+WMMV+c/TH61/UB4M8XWPjvwvZavptxHdWV/Es0UsbAqwPuK/k3/AG4P2HvGP7Enxg1DQdf026gto5mNndFTsmjzlSD9MV9Df8E3/wDgvR8Rf2HdOg8O6tAvjDwihCpb3Up82zX/AGGHJ+hNcGHr+z9yZ2V6XtPeif0rUV+ZHg7/AIOkPgjrekLJqGjeKdPugPnjaGLGe+P3nSuA+PP/AAdZeDdK0iWL4f8Ag3U9S1DBCvqgEUIPqCjk/pXc8TSSvc4/YVOx+tOueI7DwzaefqF5b2UGdvmTOEXP1NWLK9i1G2SaCRJopBlXQ5DD2Nfyp/tZ/wDBVH4z/ts+LfN1bX7+0tTJm207TpDFHF6DK4LfjX7ff8EBPFHxZ179kK2i+JNlcRW1s23Sri7J+0Tw9t2e2AMHOeamliVUlypF1KDhHmbPvOiiiuk5wooooAKKKKACiiigAooooAKKKKACiiigAooooA+IP+Cuf/BOKz/ac+H0/ijQ7ZYvFOlRmTci83CDkg/rX4b+IfD934V1m4sL6CS3urVzHJG4wVIODX9UssazRsjAMrDBB7ivyq/4LS/8Eyd8dx8SfBlhyMvqNvEv4lsD8a+L4kya98XRWvVfqfuPhdxz7CSyjHS91/A30fb07H5ofBH40a38A/iNp3iTQbqS2vbCUP8AKeJB3U+1fYH7Vfwg0H9uz4M/8Lp+H8McHiO1jA8SaREPn8wYzKF/4F156V8JyxNBKyOCrISpB7GvWv2Pf2qNX/Zc+JsGo2krSaVeEQajaMcx3ELcMCOnQmvksPXik6VX4H+D7r+tT9pzrLas5Rx+C0rQ+6cesX69H0Z5LLE0EhR1KspwQRgg02vrv9vL9ljSdX8PwfFr4cqLrwprn728ghGTYStyykDoMk+nSvkSsq1GVKfLL/h/M9HK8ypY6gq1PTo090+qfmgoop0MTXEqoilnY4AA5JrE9EbXqH7L/wCyz4h/ad8dwaZpVu6WasGurtxiKBB94ljx0zXpH7NH/BOjX/iTZL4k8XsPCvg+3HmTXd5+6aRfRQ2M/nXf/Fv9rrTPAPhr/hV3wJsJhDcf6Pc6jFHm5vSeDggZx+J612U8OklUr6Lt1fp/mfLY/PJVJPCZZaVTrL7MPNvv5E/7WX7Qvh/9l74Wt8G/hi6PJIoXW9VhPzXMmMMikdslq82/Zn/Yvh1zw/P4/wDiTdtoPgzTv3zCX5Z9RP8AcjByTn1x2NeifCf9kbw1+zV4cHxB+Ndwr3sg8+z0Uvma4Y/MC4PPp2714Z+0z+1T4k/aq8XQ2MKSQaLbN5WmaTaqRHGo4GFHBPXt3res7NVKy16R7Lz/AKuzycthKcJYXLZPlvepWe8n15e789olr9rf9ryb463dtoeg2v8AYHgXRB5WmaXEcKqj+JvVjx+Vc3+zj+yv4o/aW8Ww6doto625YeddyLthhXuSxwP1r2z9nr/gnEbPQYvGXxZvU8J+GIx5y287bLm6A5wFJBr1SP4ieKvj/GPh7+z34Zm0LwvnyLjVFiMb3AHBYyADryetJYeU5c9fd7Jbv0XRGtXOaGFovCZVa0fim/gj3bf2peX3n6B/8E5f+Cffgv8AZR8CQXlg1prGvXKD7RfqQ+G7hSPpX1LXzR/wTb+Amq/s9fDibRNb8W/8JFqi/PPCZvM+ysecfePr+tfS9fpeWwjDDRUYcvkfy1xJiKlbMas6lX2uvxbX9F2Ciiiu88I8i/bo+AF7+0/+zD4l8E6fcJa3WswiNJHHC4Nfjh/xCu/EP/oaNL/74P8AjX72UVjUoQm7yNYVZQVon4J/8QrvxD/6GjS/++D/AI0f8QrvxD/6GjS/++D/AI1+9lFZ/U6XYv6zUPwT/wCIV34h/wDQ0aX/AN8H/Gj/AIhXfiH/ANDRpf8A3wf8a/eyij6nS7B9ZqH4J/8AEK78Q/8AoaNL/wC+D/jV3w7/AMGt3xB0jXbW5bxPpZWCQOQEPOPxr936KPqdIPrNQxPhv4afwb4A0fSZWDyafZxW7MOhKqAf5V8nf8FJP+CNHgT9v2J9UYroXioLhb6NeJO43jBJ7/nX2ZRXRKCkrSMYycXdH88nxT/4NiPjT4c1eSPw7Pp2t2wY7ZBIseR9GYVZ+GH/AAbA/GLxDq0UfiC903RrYsN8hcSbR/wFjX9CVFc31Kne5v8AWpnwb+wN/wAEE/hh+x5fW2tasieLPEsABE86/uY2H8SrgHOfU+ld1/wVZ/4Jjwf8FE/hLp+iWupW+iX2kvvtpWjJXHpgfhX1xRW/socvJbQx9pLm5r6n4TP/AMGpvjQMceNdKI9fKavtT/gjF/wSE13/AIJp+OvG2q6vrtpq6eJ7O2to1hQr5ZieRiTn13/pX6C0VEMNTi+ZIuVeclZiHpX8m3/BVX/lIZ8VP+wx/wC0o6/rB1TV7XRrVpru4it4l6vIwUD8a/k5/wCCo9/Dqf8AwUB+KM9vKk0Mmr5R0bKsPKj6GufH/CjbB/Ez9Iv+Dd79mbwh+1P+yF4z8N+MNKg1KxnvZApdfnhJx8yn16flUP7Un/BrPfrrNzf/AA38SQyWkrM0dlcj50z23YArt/8Ag1f8U6bpnwV8T2lxfWsNzJfsVieQB26dBX69q24ZHIq6VGE6S5kTUqyhUdj+cY/8G0f7QY1Ty/sWnfZ8/wCt+0R9PpvzX0h+yh/wa3XMGt22o/EvxBF9miYM9jbD5nxzjOCK/aqiqjg6adyXiZs4b4Bfs6+E/wBmjwFbeHfCWlW+mWFuoXEa4aQgdWPrXc0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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Logo.jpg](attachment:Logo.jpg)\n",
    "\n",
    "\n",
    "<font size=\"5\">The scenario presented here is that you've recently been hired as a Data Analyst for an online book store.</font>\n",
    "\n",
    "You are asked to respond to a list of queries by Antoine and Julie from the marketing department.\n",
    "\n",
    "There are 3 csv files provided, namely:\n",
    "\n",
    "1. Customers \n",
    "2. Products\n",
    "3. Transactions \n",
    "\n",
    "Antoine needs information on the sales figures and customer profiles, namely:\n",
    "1. The evolution of the sales figures since the launch of the website.\n",
    "2. The sales figures per age and per session.\n",
    "3. A list of best and worst selling books and categories.\n",
    "4. The customer profiles by\n",
    "    1. age,\n",
    "    2. gender,\n",
    "    3. the proportion of amount spent using a Lorenz curve and gini coefficient.\n",
    "\n",
    "Julie needs information on:\n",
    "\n",
    "5. The relationship between the gender of the customer and the category of book bought.\n",
    "6. The correlation between the age of the customer and \n",
    "    1. the amount spent,\n",
    "    2. the frequency of purchase,\n",
    "    3. the average of shopping basket amount,\n",
    "    4. the book categories.\n",
    "7. The probability of a client buying product 0_525 if they've bought product 2_159."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 1: Importing libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Step 1: IMPORTING LIBRARIES\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import statsmodels.api as sm\n",
    "import seaborn as sns\n",
    "import datetime as dt\n",
    "sns.set()\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Step 2: IMPORTING & ANALYSING DATA\n",
    "## 1. The customers file with:\n",
    "    1. Client_id\n",
    "    2. The sex (gender) of the client\n",
    "    3. Year of birth"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c_7839</td>\n",
       "      <td>f</td>\n",
       "      <td>1975</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c_1699</td>\n",
       "      <td>f</td>\n",
       "      <td>1984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c_5961</td>\n",
       "      <td>f</td>\n",
       "      <td>1962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c_5320</td>\n",
       "      <td>m</td>\n",
       "      <td>1943</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  client_id sex  birth\n",
       "0    c_4410   f   1967\n",
       "1    c_7839   f   1975\n",
       "2    c_1699   f   1984\n",
       "3    c_5961   f   1962\n",
       "4    c_5320   m   1943"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers = pd.read_csv(r\"customers.csv\")\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "client_id    object\n",
       "sex          object\n",
       "birth         int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Normally I would change the birth year to a datetime type but since we need to work with it to calculate the customers' ages\n",
    "I have left it as it is."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Format birth to datetime\n",
    "#customers['birth'] = pd.to_datetime(customers['birth'], format= '%Y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8623</td>\n",
       "      <td>8623</td>\n",
       "      <td>8623.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>8623</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>4491</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1978.280877</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16.919535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1929.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1966.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1979.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1992.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       client_id   sex        birth\n",
       "count       8623  8623  8623.000000\n",
       "unique      8623     2          NaN\n",
       "top       c_4410     f          NaN\n",
       "freq           1  4491          NaN\n",
       "mean         NaN   NaN  1978.280877\n",
       "std          NaN   NaN    16.919535\n",
       "min          NaN   NaN  1929.000000\n",
       "25%          NaN   NaN  1966.000000\n",
       "50%          NaN   NaN  1979.000000\n",
       "75%          NaN   NaN  1992.000000\n",
       "max          NaN   NaN  2004.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Having a look at the distribution of our customers by gender and age/age group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "customers.sex.value_counts().plot(kind='pie', autopct='%.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth</th>\n",
       "      <th>age</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>c_4410</td>\n",
       "      <td>f</td>\n",
       "      <td>1967</td>\n",
       "      <td>55</td>\n",
       "      <td>46-55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>c_7839</td>\n",
       "      <td>f</td>\n",
       "      <td>1975</td>\n",
       "      <td>47</td>\n",
       "      <td>46-55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>c_1699</td>\n",
       "      <td>f</td>\n",
       "      <td>1984</td>\n",
       "      <td>38</td>\n",
       "      <td>36-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>c_5961</td>\n",
       "      <td>f</td>\n",
       "      <td>1962</td>\n",
       "      <td>60</td>\n",
       "      <td>56-65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>c_5320</td>\n",
       "      <td>m</td>\n",
       "      <td>1943</td>\n",
       "      <td>79</td>\n",
       "      <td>76-100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  client_id sex  birth  age age_group\n",
       "0    c_4410   f   1967   55     46-55\n",
       "1    c_7839   f   1975   47     46-55\n",
       "2    c_1699   f   1984   38     36-45\n",
       "3    c_5961   f   1962   60     56-65\n",
       "4    c_5320   m   1943   79    76-100"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customers[\"age\"] = dt.date.today().year - customers[\"birth\"]\n",
    "customers[\"age_group\"] = pd.cut(customers[\"age\"], bins=[15,25,35,45,55,65,75,100],\\\n",
    "                                labels = [\"16-25\",\"26-35\",\"36-45\",\"46-55\",\"56-65\",\"66-75\",\"76-100\"])\n",
    "customers.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1440x504 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum age: 18 and maximum age: 93\n"
     ]
    }
   ],
   "source": [
    "plt.figure(figsize=(20,7))\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "sns.histplot(customers[\"age\"])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "sns.histplot(customers[\"age_group\"])\n",
    "plt.show()\n",
    "print(\"Minimum age:\",customers[\"age\"].min(), \"and maximum age:\", customers[\"age\"].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. The products file with:\n",
    "    1. Product ID\n",
    "    2. Price\n",
    "    3. Category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_1421</td>\n",
       "      <td>19.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0_1368</td>\n",
       "      <td>5.13</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_731</td>\n",
       "      <td>17.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1_587</td>\n",
       "      <td>4.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_1507</td>\n",
       "      <td>3.99</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id_prod  price  categ\n",
       "0  0_1421  19.99      0\n",
       "1  0_1368   5.13      0\n",
       "2   0_731  17.99      0\n",
       "3   1_587   4.99      1\n",
       "4  0_1507   3.99      0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products = pd.read_csv(r\"products.csv\")\n",
    "products.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_prod     object\n",
       "price      float64\n",
       "categ        int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3287</td>\n",
       "      <td>3287.000000</td>\n",
       "      <td>3287.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3287</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>0_1421</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21.856641</td>\n",
       "      <td>0.370246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>29.847908</td>\n",
       "      <td>0.615387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>6.990000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>13.060000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>22.990000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_prod        price        categ\n",
       "count     3287  3287.000000  3287.000000\n",
       "unique    3287          NaN          NaN\n",
       "top     0_1421          NaN          NaN\n",
       "freq         1          NaN          NaN\n",
       "mean       NaN    21.856641     0.370246\n",
       "std        NaN    29.847908     0.615387\n",
       "min        NaN    -1.000000     0.000000\n",
       "25%        NaN     6.990000     0.000000\n",
       "50%        NaN    13.060000     0.000000\n",
       "75%        NaN    22.990000     1.000000\n",
       "max        NaN   300.000000     2.000000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "products.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>731</th>\n",
       "      <td>T_0</td>\n",
       "      <td>-1.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id_prod  price  categ\n",
       "731     T_0   -1.0      0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Having a look at the product(s) with a negative price\n",
    "products[products[\"price\"]<0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<span style='background :black'><font color='white'>The product ID indicates this is a test product</font></span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taking a look at the category distribution\n",
    "products.categ.value_counts().plot(kind='pie', autopct='%.2f')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Taking a look at the range of prices\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.histplot(data =products, x=\"price\" )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Having a look at the price per category and identify any potential outliers\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.boxplot(x = 'categ', y = 'price', data = products)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>We can observe that the prices have very little overlap between the categories, but not none.\n",
    "A potential explanation for this is that the categories are based on price but as more products were added this convention wasn't always adhered to.</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. The transactions file with:\n",
    "    1. Product ID\n",
    "    2. The date and time of the transaction\n",
    "    3. Session ID\n",
    "    4. Client ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0_1518</td>\n",
       "      <td>2022-05-20 13:21:29.043970</td>\n",
       "      <td>s_211425</td>\n",
       "      <td>c_103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1_251</td>\n",
       "      <td>2022-02-02 07:55:19.149409</td>\n",
       "      <td>s_158752</td>\n",
       "      <td>c_8534</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0_1277</td>\n",
       "      <td>2022-06-18 15:44:33.155329</td>\n",
       "      <td>s_225667</td>\n",
       "      <td>c_6714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2_209</td>\n",
       "      <td>2021-06-24 04:19:29.835891</td>\n",
       "      <td>s_52962</td>\n",
       "      <td>c_6941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0_1509</td>\n",
       "      <td>2023-01-11 08:22:08.194479</td>\n",
       "      <td>s_325227</td>\n",
       "      <td>c_4232</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  id_prod                        date session_id client_id\n",
       "0  0_1518  2022-05-20 13:21:29.043970   s_211425     c_103\n",
       "1   1_251  2022-02-02 07:55:19.149409   s_158752    c_8534\n",
       "2  0_1277  2022-06-18 15:44:33.155329   s_225667    c_6714\n",
       "3   2_209  2021-06-24 04:19:29.835891    s_52962    c_6941\n",
       "4  0_1509  2023-01-11 08:22:08.194479   s_325227    c_4232"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions = pd.read_csv(r\"transactions.csv\")\n",
    "transactions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_prod       object\n",
       "date          object\n",
       "session_id    object\n",
       "client_id     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>679532</td>\n",
       "      <td>679532</td>\n",
       "      <td>679532</td>\n",
       "      <td>679532</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3267</td>\n",
       "      <td>679371</td>\n",
       "      <td>342316</td>\n",
       "      <td>8602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>1_369</td>\n",
       "      <td>test_2021-03-01 02:30:02.237413</td>\n",
       "      <td>s_0</td>\n",
       "      <td>c_1609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2252</td>\n",
       "      <td>13</td>\n",
       "      <td>200</td>\n",
       "      <td>25488</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_prod                             date session_id client_id\n",
       "count   679532                           679532     679532    679532\n",
       "unique    3267                           679371     342316      8602\n",
       "top      1_369  test_2021-03-01 02:30:02.237413        s_0    c_1609\n",
       "freq      2252                               13        200     25488"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3019</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237419</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5138</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237425</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9668</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237437</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10728</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237436</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15292</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237430</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>657830</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237417</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662081</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237427</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670680</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237449</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671647</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237424</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679180</th>\n",
       "      <td>T_0</td>\n",
       "      <td>test_2021-03-01 02:30:02.237425</td>\n",
       "      <td>s_0</td>\n",
       "      <td>ct_1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>200 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_prod                             date session_id client_id\n",
       "3019       T_0  test_2021-03-01 02:30:02.237419        s_0      ct_0\n",
       "5138       T_0  test_2021-03-01 02:30:02.237425        s_0      ct_0\n",
       "9668       T_0  test_2021-03-01 02:30:02.237437        s_0      ct_1\n",
       "10728      T_0  test_2021-03-01 02:30:02.237436        s_0      ct_0\n",
       "15292      T_0  test_2021-03-01 02:30:02.237430        s_0      ct_0\n",
       "...        ...                              ...        ...       ...\n",
       "657830     T_0  test_2021-03-01 02:30:02.237417        s_0      ct_0\n",
       "662081     T_0  test_2021-03-01 02:30:02.237427        s_0      ct_1\n",
       "670680     T_0  test_2021-03-01 02:30:02.237449        s_0      ct_1\n",
       "671647     T_0  test_2021-03-01 02:30:02.237424        s_0      ct_1\n",
       "679180     T_0  test_2021-03-01 02:30:02.237425        s_0      ct_1\n",
       "\n",
       "[200 rows x 4 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.loc[transactions['session_id'] == 's_0']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When taking a closer look at the sessions with id s_0 we find they're the test sessions and by removing them we'll also eliminate the test product id's and client id's"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop test data\n",
    "transactions.drop(transactions.loc[transactions['session_id'] == 's_0'].index, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "#format date\n",
    "transactions['date'] = pd.to_datetime(transactions['date'], format= '%Y-%m-%d %H:%M:%S')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(679332, 4)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_prod               object\n",
       "date          datetime64[ns]\n",
       "session_id            object\n",
       "client_id             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "transactions.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merging the files into a complete dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "      <th>sex</th>\n",
       "      <th>birth</th>\n",
       "      <th>age</th>\n",
       "      <th>age_group</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>679332</td>\n",
       "      <td>679332</td>\n",
       "      <td>679332</td>\n",
       "      <td>679332</td>\n",
       "      <td>679111.000000</td>\n",
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       "      <td>679332</td>\n",
       "      <td>679332.000000</td>\n",
       "      <td>679332.000000</td>\n",
       "      <td>679332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3266</td>\n",
       "      <td>679332</td>\n",
       "      <td>342315</td>\n",
       "      <td>8600</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>1_369</td>\n",
       "      <td>2022-05-20 13:21:29.043970</td>\n",
       "      <td>s_118668</td>\n",
       "      <td>c_1609</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>m</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>2252</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>25488</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>340930</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>235916</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>first</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-03-01 00:01:07.843138</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2023-02-28 23:58:30.792755</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.454773</td>\n",
       "      <td>0.441953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1977.811139</td>\n",
       "      <td>44.188861</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.328998</td>\n",
       "      <td>0.595042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.574553</td>\n",
       "      <td>13.574553</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.620000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1929.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.870000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1970.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.990000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1980.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>18.990000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1987.000000</td>\n",
       "      <td>52.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>300.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2004.000000</td>\n",
       "      <td>93.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_prod                        date session_id client_id  \\\n",
       "count   679332                      679332     679332    679332   \n",
       "unique    3266                      679332     342315      8600   \n",
       "top      1_369  2022-05-20 13:21:29.043970   s_118668    c_1609   \n",
       "freq      2252                           1         14     25488   \n",
       "first      NaN  2021-03-01 00:01:07.843138        NaN       NaN   \n",
       "last       NaN  2023-02-28 23:58:30.792755        NaN       NaN   \n",
       "mean       NaN                         NaN        NaN       NaN   \n",
       "std        NaN                         NaN        NaN       NaN   \n",
       "min        NaN                         NaN        NaN       NaN   \n",
       "25%        NaN                         NaN        NaN       NaN   \n",
       "50%        NaN                         NaN        NaN       NaN   \n",
       "75%        NaN                         NaN        NaN       NaN   \n",
       "max        NaN                         NaN        NaN       NaN   \n",
       "\n",
       "                price          categ     sex          birth            age  \\\n",
       "count   679111.000000  679111.000000  679332  679332.000000  679332.000000   \n",
       "unique            NaN            NaN       2            NaN            NaN   \n",
       "top               NaN            NaN       m            NaN            NaN   \n",
       "freq              NaN            NaN  340930            NaN            NaN   \n",
       "first             NaN            NaN     NaN            NaN            NaN   \n",
       "last              NaN            NaN     NaN            NaN            NaN   \n",
       "mean        17.454773       0.441953     NaN    1977.811139      44.188861   \n",
       "std         18.328998       0.595042     NaN      13.574553      13.574553   \n",
       "min          0.620000       0.000000     NaN    1929.000000      18.000000   \n",
       "25%          8.870000       0.000000     NaN    1970.000000      35.000000   \n",
       "50%         13.990000       0.000000     NaN    1980.000000      42.000000   \n",
       "75%         18.990000       1.000000     NaN    1987.000000      52.000000   \n",
       "max        300.000000       2.000000     NaN    2004.000000      93.000000   \n",
       "\n",
       "       age_group  \n",
       "count     679332  \n",
       "unique         7  \n",
       "top        36-45  \n",
       "freq      235916  \n",
       "first        NaN  \n",
       "last         NaN  \n",
       "mean         NaN  \n",
       "std          NaN  \n",
       "min          NaN  \n",
       "25%          NaN  \n",
       "50%          NaN  \n",
       "75%          NaN  \n",
       "max          NaN  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "full_data = transactions.merge(products, on='id_prod', how='left').merge(customers, on='client_id', how='left')\n",
    "full_data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are missing values for this product: ['0_2245']\n"
     ]
    }
   ],
   "source": [
    "#We're missing values for the price and categ features\n",
    "missing_values = (full_data['price'].isnull()) | (full_data['categ'].isnull())\n",
    "print(\"There are missing values for this product:\", full_data[missing_values][\"id_prod\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "    <tr>\n",
       "      <th>2633</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2022-09-23 07:22:38.636773</td>\n",
       "      <td>s_272266</td>\n",
       "      <td>c_4746</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>m</td>\n",
       "      <td>1940</td>\n",
       "      <td>82</td>\n",
       "      <td>76-100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10103</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2022-07-23 09:24:14.133889</td>\n",
       "      <td>s_242482</td>\n",
       "      <td>c_6713</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>1963</td>\n",
       "      <td>59</td>\n",
       "      <td>56-65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11723</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2022-12-03 03:26:35.696673</td>\n",
       "      <td>s_306338</td>\n",
       "      <td>c_5108</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>m</td>\n",
       "      <td>1978</td>\n",
       "      <td>44</td>\n",
       "      <td>36-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15670</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2021-08-16 11:33:25.481411</td>\n",
       "      <td>s_76493</td>\n",
       "      <td>c_1391</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>m</td>\n",
       "      <td>1991</td>\n",
       "      <td>31</td>\n",
       "      <td>26-35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16372</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2022-07-16 05:53:01.627491</td>\n",
       "      <td>s_239078</td>\n",
       "      <td>c_7954</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>m</td>\n",
       "      <td>1973</td>\n",
       "      <td>49</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>669533</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2021-08-25 09:06:03.504061</td>\n",
       "      <td>s_80395</td>\n",
       "      <td>c_131</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>m</td>\n",
       "      <td>1981</td>\n",
       "      <td>41</td>\n",
       "      <td>36-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>670484</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2022-03-06 19:59:19.462288</td>\n",
       "      <td>s_175311</td>\n",
       "      <td>c_4167</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>f</td>\n",
       "      <td>1979</td>\n",
       "      <td>43</td>\n",
       "      <td>36-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671088</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2022-05-16 11:35:20.319501</td>\n",
       "      <td>s_209381</td>\n",
       "      <td>c_4453</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>m</td>\n",
       "      <td>1981</td>\n",
       "      <td>41</td>\n",
       "      <td>36-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>675480</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2022-02-11 09:05:43.952857</td>\n",
       "      <td>s_163405</td>\n",
       "      <td>c_1098</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>m</td>\n",
       "      <td>1986</td>\n",
       "      <td>36</td>\n",
       "      <td>36-45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677797</th>\n",
       "      <td>0_2245</td>\n",
       "      <td>2021-12-14 22:34:54.589921</td>\n",
       "      <td>s_134446</td>\n",
       "      <td>c_4854</td>\n",
       "      <td>10.638188</td>\n",
       "      <td>0.0</td>\n",
       "      <td>m</td>\n",
       "      <td>1968</td>\n",
       "      <td>54</td>\n",
       "      <td>46-55</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>221 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_prod                       date session_id client_id      price  \\\n",
       "2633    0_2245 2022-09-23 07:22:38.636773   s_272266    c_4746  10.638188   \n",
       "10103   0_2245 2022-07-23 09:24:14.133889   s_242482    c_6713  10.638188   \n",
       "11723   0_2245 2022-12-03 03:26:35.696673   s_306338    c_5108  10.638188   \n",
       "15670   0_2245 2021-08-16 11:33:25.481411    s_76493    c_1391  10.638188   \n",
       "16372   0_2245 2022-07-16 05:53:01.627491   s_239078    c_7954  10.638188   \n",
       "...        ...                        ...        ...       ...        ...   \n",
       "669533  0_2245 2021-08-25 09:06:03.504061    s_80395     c_131  10.638188   \n",
       "670484  0_2245 2022-03-06 19:59:19.462288   s_175311    c_4167  10.638188   \n",
       "671088  0_2245 2022-05-16 11:35:20.319501   s_209381    c_4453  10.638188   \n",
       "675480  0_2245 2022-02-11 09:05:43.952857   s_163405    c_1098  10.638188   \n",
       "677797  0_2245 2021-12-14 22:34:54.589921   s_134446    c_4854  10.638188   \n",
       "\n",
       "        categ sex  birth  age age_group  \n",
       "2633      0.0   m   1940   82    76-100  \n",
       "10103     0.0   f   1963   59     56-65  \n",
       "11723     0.0   m   1978   44     36-45  \n",
       "15670     0.0   m   1991   31     26-35  \n",
       "16372     0.0   m   1973   49     46-55  \n",
       "...       ...  ..    ...  ...       ...  \n",
       "669533    0.0   m   1981   41     36-45  \n",
       "670484    0.0   f   1979   43     36-45  \n",
       "671088    0.0   m   1981   41     36-45  \n",
       "675480    0.0   m   1986   36     36-45  \n",
       "677797    0.0   m   1968   54     46-55  \n",
       "\n",
       "[221 rows x 10 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Correcting product 0_2245, by assigning it its correct category (0, as indicated by the product id)\n",
    "#and then assigning it the mean price for that category:\n",
    "full_data.loc[full_data.id_prod == \"0_2245\", \"categ\"] = 0\n",
    "full_data['price'] = full_data['price'].fillna(full_data.groupby('categ')['price'].transform('mean'))\n",
    "full_data.loc[full_data[\"id_prod\"] == \"0_2245\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking to see if any products have never been sold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>date</th>\n",
       "      <th>session_id</th>\n",
       "      <th>client_id</th>\n",
       "      <th>price</th>\n",
       "      <th>categ</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>679332</th>\n",
       "      <td>0_1016</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.06</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
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       "    <tr>\n",
       "      <th>679333</th>\n",
       "      <td>0_1780</td>\n",
       "      <td>NaT</td>\n",
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       "      <th>679334</th>\n",
       "      <td>T_0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
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       "    <tr>\n",
       "      <th>679335</th>\n",
       "      <td>0_1062</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.08</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679336</th>\n",
       "      <td>0_1119</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
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       "    <tr>\n",
       "      <th>679337</th>\n",
       "      <td>0_1014</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.15</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
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       "    <tr>\n",
       "      <th>679338</th>\n",
       "      <td>1_0</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.82</td>\n",
       "      <td>1.0</td>\n",
       "      <td>right_only</td>\n",
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       "    <tr>\n",
       "      <th>679339</th>\n",
       "      <td>0_1318</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.92</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679340</th>\n",
       "      <td>0_1800</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679341</th>\n",
       "      <td>0_1645</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679342</th>\n",
       "      <td>0_322</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679343</th>\n",
       "      <td>0_1620</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679344</th>\n",
       "      <td>0_1025</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679345</th>\n",
       "      <td>2_87</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>220.99</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679346</th>\n",
       "      <td>1_394</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.73</td>\n",
       "      <td>1.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679347</th>\n",
       "      <td>2_72</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>141.32</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679348</th>\n",
       "      <td>0_310</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.94</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679349</th>\n",
       "      <td>0_1624</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.50</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679350</th>\n",
       "      <td>2_86</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>132.36</td>\n",
       "      <td>2.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679351</th>\n",
       "      <td>0_299</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679352</th>\n",
       "      <td>0_510</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>23.66</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>679353</th>\n",
       "      <td>0_2308</td>\n",
       "      <td>NaT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>right_only</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       id_prod date session_id client_id   price  categ      _merge\n",
       "679332  0_1016  NaT        NaN       NaN   35.06    0.0  right_only\n",
       "679333  0_1780  NaT        NaN       NaN    1.67    0.0  right_only\n",
       "679334     T_0  NaT        NaN       NaN   -1.00    0.0  right_only\n",
       "679335  0_1062  NaT        NaN       NaN   20.08    0.0  right_only\n",
       "679336  0_1119  NaT        NaN       NaN    2.99    0.0  right_only\n",
       "679337  0_1014  NaT        NaN       NaN    1.15    0.0  right_only\n",
       "679338     1_0  NaT        NaN       NaN   31.82    1.0  right_only\n",
       "679339  0_1318  NaT        NaN       NaN   20.92    0.0  right_only\n",
       "679340  0_1800  NaT        NaN       NaN   22.05    0.0  right_only\n",
       "679341  0_1645  NaT        NaN       NaN    2.99    0.0  right_only\n",
       "679342   0_322  NaT        NaN       NaN    2.99    0.0  right_only\n",
       "679343  0_1620  NaT        NaN       NaN    0.80    0.0  right_only\n",
       "679344  0_1025  NaT        NaN       NaN   24.99    0.0  right_only\n",
       "679345    2_87  NaT        NaN       NaN  220.99    2.0  right_only\n",
       "679346   1_394  NaT        NaN       NaN   39.73    1.0  right_only\n",
       "679347    2_72  NaT        NaN       NaN  141.32    2.0  right_only\n",
       "679348   0_310  NaT        NaN       NaN    1.94    0.0  right_only\n",
       "679349  0_1624  NaT        NaN       NaN   24.50    0.0  right_only\n",
       "679350    2_86  NaT        NaN       NaN  132.36    2.0  right_only\n",
       "679351   0_299  NaT        NaN       NaN   22.99    0.0  right_only\n",
       "679352   0_510  NaT        NaN       NaN   23.66    0.0  right_only\n",
       "679353  0_2308  NaT        NaN       NaN   20.28    0.0  right_only"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Unsold products:\n",
    "trans_prod_nan = transactions.merge(products, on='id_prod', how='outer',indicator=True)\n",
    "trans_prod_nan = trans_prod_nan[trans_prod_nan._merge == \"right_only\"]\n",
    "trans_prod_nan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Answering the questions:\n",
    "\n",
    "### Question 1: The evolution of the sales figures since the launch of the website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Daily sales figures with a 7 day (weekly) rolling average\n",
    "trans_prod_day = full_data.groupby(pd.Grouper(key='date',freq='D')).agg({'price':'sum'})\n",
    "trans_prod_day['RollAvg'] = trans_prod_day.rolling(7, min_periods=1).mean()\n",
    "colors = ['green', 'red']\n",
    "trans_prod_day.plot(color=colors, linewidth=2, figsize=(12,6))\n",
    "plt.legend(labels =['amount', 'rolling average amount'], fontsize=14)\n",
    "plt.title('Daily amount spent on our website', fontsize=20)\n",
    "plt.xlabel('Day', fontsize=16)\n",
    "plt.ylabel('Sales amount', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'monthly'}, xlabel='date'>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Weekly and monthly Sales figures\n",
    "df = full_data.copy()\n",
    "df.index = full_data['date']\n",
    "df = df.groupby(pd.Grouper(key='date',freq='D')).agg({'price':'sum'})\n",
    "\n",
    "weekly = df.resample('W').sum()\n",
    "monthly = df.resample('M').sum()\n",
    "fig,axs = plt.subplots(2,1)\n",
    "weekly.price.plot(figsize = (12,8), title=\"weekly\",ax=axs[0])\n",
    "monthly.price.plot(figsize = (12,8), title=\"monthly\",ax=axs[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#why the dip in October 2021?\n",
    "#checking categories in October\n",
    "trans_oct = full_data[(full_data['date'] > \"2021-10-01\") & (full_data['date'] < \"2021-11-01\")]\n",
    "trans_oct_categ = trans_oct.set_index('date').groupby([pd.Grouper(freq='D'), 'categ']).count()\n",
    "trans_oct_categ.reset_index(inplace=True)\n",
    "trans_oct_categ.index = pd.DatetimeIndex(trans_oct_categ.date)\n",
    "trans_oct_categ.drop(columns=['session_id','client_id','price','date'])\n",
    "\n",
    "plt.figure(figsize=(10,7))\n",
    "sns.barplot(data=trans_oct_categ,x=\"date\",y=\"id_prod\",hue=\"categ\").set(xticklabels=[])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<font color='blue'>As we can see, products from category 1 were not sold for much of the month of October 2021.\n",
    "Why this is is not clear, it could be a glitch, it could be an error, we could have run out of stock (although this seems unlikely). Further investigation would be necessary to find the cause but fortunately the problem seems to have been resolved by the end of the month</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2a: Sales by category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_54728_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >categ</th>\n",
       "      <th class=\"col_heading level0 col1\" >price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_54728_level0_row0\" class=\"row_heading level0 row0\" >0</th>\n",
       "      <td id=\"T_54728_row0_col0\" class=\"data row0 col0\" >0.0</td>\n",
       "      <td id=\"T_54728_row0_col1\" class=\"data row0 col1\" >4422082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54728_level0_row1\" class=\"row_heading level0 row1\" >1</th>\n",
       "      <td id=\"T_54728_row1_col0\" class=\"data row1 col0\" >1.0</td>\n",
       "      <td id=\"T_54728_row1_col1\" class=\"data row1 col1\" >4653722.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_54728_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_54728_row2_col0\" class=\"data row2 col0\" >2.0</td>\n",
       "      <td id=\"T_54728_row2_col1\" class=\"data row2 col1\" >2780275.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x16d31e7e2b0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_categ = full_data.groupby(['categ'])[['price']].sum().reset_index()\n",
    "ca_categ.style.format(\"{:.1f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(data=ca_categ, x=\"price\",labels = 'categ', autopct='%.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2b: Sales by gender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sex</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>f</td>\n",
       "      <td>5860927.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>m</td>\n",
       "      <td>5995151.92</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  sex      price\n",
       "0   f 5860927.80\n",
       "1   m 5995151.92"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#in order to avoid the summed amounts to display in scientific notation I'm changing the display format\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "\n",
    "ca_sexe = full_data.groupby(['sex'])[['price']].sum().reset_index()\n",
    "ca_sexe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(data=ca_sexe, x=\"price\",labels = 'sex', autopct='%.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2c: Sales by age group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age_group</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16-25</td>\n",
       "      <td>2190905.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>26-35</td>\n",
       "      <td>2370752.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>36-45</td>\n",
       "      <td>3114034.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>46-55</td>\n",
       "      <td>2161898.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>56-65</td>\n",
       "      <td>1056079.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>66-75</td>\n",
       "      <td>666548.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>76-100</td>\n",
       "      <td>295861.48</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  age_group      price\n",
       "0     16-25 2190905.79\n",
       "1     26-35 2370752.43\n",
       "2     36-45 3114034.38\n",
       "3     46-55 2161898.36\n",
       "4     56-65 1056079.28\n",
       "5     66-75  666548.01\n",
       "6    76-100  295861.48"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calculate Sales by age group\n",
    "ca_age_group = full_data.groupby(['age_group'])[['price']].sum().reset_index()\n",
    "ca_age_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 936x504 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(13,7))\n",
    "sns.barplot(data = ca_age_group, x = \"age_group\", y = \"price\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(data=ca_age_group, x=\"price\",labels = 'age_group', autopct='%.0f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2d: Average shopping basket per session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "price   34.63\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "session_mean = full_data.groupby(['session_id'])[['price']].sum().mean()\n",
    "print(session_mean)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3a: Book sales tops & flops (by product)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3097</th>\n",
       "      <td>2_159</td>\n",
       "      <td>94893.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3071</th>\n",
       "      <td>2_135</td>\n",
       "      <td>69334.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3046</th>\n",
       "      <td>2_112</td>\n",
       "      <td>65407.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3035</th>\n",
       "      <td>2_102</td>\n",
       "      <td>60736.78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3153</th>\n",
       "      <td>2_209</td>\n",
       "      <td>56971.86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2620</th>\n",
       "      <td>1_395</td>\n",
       "      <td>54356.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2592</th>\n",
       "      <td>1_369</td>\n",
       "      <td>54025.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3044</th>\n",
       "      <td>2_110</td>\n",
       "      <td>53846.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3202</th>\n",
       "      <td>2_39</td>\n",
       "      <td>53060.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3105</th>\n",
       "      <td>2_166</td>\n",
       "      <td>52449.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id_prod    price\n",
       "3097   2_159 94893.50\n",
       "3071   2_135 69334.95\n",
       "3046   2_112 65407.76\n",
       "3035   2_102 60736.78\n",
       "3153   2_209 56971.86\n",
       "2620   1_395 54356.25\n",
       "2592   1_369 54025.48\n",
       "3044   2_110 53846.25\n",
       "3202    2_39 53060.85\n",
       "3105   2_166 52449.12"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Top earners\n",
    "full_data_prodprice = full_data.groupby('id_prod')['price'].sum().reset_index()\n",
    "full_data_prodprice.sort_values('price', ascending=False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id_prod</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>595</th>\n",
       "      <td>0_1539</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>313</th>\n",
       "      <td>0_1284</td>\n",
       "      <td>1.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>719</th>\n",
       "      <td>0_1653</td>\n",
       "      <td>1.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1785</th>\n",
       "      <td>0_541</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2080</th>\n",
       "      <td>0_807</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>665</th>\n",
       "      <td>0_1601</td>\n",
       "      <td>1.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>0_1728</td>\n",
       "      <td>2.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>549</th>\n",
       "      <td>0_1498</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2180</th>\n",
       "      <td>0_898</td>\n",
       "      <td>2.54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>925</th>\n",
       "      <td>0_1840</td>\n",
       "      <td>2.56</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id_prod  price\n",
       "595   0_1539   0.99\n",
       "313   0_1284   1.38\n",
       "719   0_1653   1.98\n",
       "1785   0_541   1.99\n",
       "2080   0_807   1.99\n",
       "665   0_1601   1.99\n",
       "802   0_1728   2.27\n",
       "549   0_1498   2.48\n",
       "2180   0_898   2.54\n",
       "925   0_1840   2.56"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lowest earners\n",
    "full_data_prodprice.sort_values('price').head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3b: Book sales tops & flops (by category)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "</style>\n",
       "<table id=\"T_eb9d4_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >categ</th>\n",
       "      <th class=\"col_heading level0 col1\" >price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_eb9d4_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_eb9d4_row0_col0\" class=\"data row0 col0\" >1.0</td>\n",
       "      <td id=\"T_eb9d4_row0_col1\" class=\"data row0 col1\" >4653722.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb9d4_level0_row1\" class=\"row_heading level0 row1\" >0</th>\n",
       "      <td id=\"T_eb9d4_row1_col0\" class=\"data row1 col0\" >0.0</td>\n",
       "      <td id=\"T_eb9d4_row1_col1\" class=\"data row1 col1\" >4422082.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_eb9d4_level0_row2\" class=\"row_heading level0 row2\" >2</th>\n",
       "      <td id=\"T_eb9d4_row2_col0\" class=\"data row2 col0\" >2.0</td>\n",
       "      <td id=\"T_eb9d4_row2_col1\" class=\"data row2 col1\" >2780275.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x16d31f84be0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ca_cat_sorted = ca_categ.sort_values('price', ascending=False)\n",
    "ca_cat_sorted.style.format(\"{:.1f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### It needs to be born in mind that category 2 is the category with the most expensive books, which likely has a large influence on the amounts sold"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4a: Client profile (age)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Finding the age counts for each unique customer\n",
    "cp_age = customers.groupby('age')['client_id'].count().reset_index()\n",
    "\n",
    "# Finding the number of times someone of a certain age has bought one of our books\n",
    "cp_age_full = full_data.groupby('age')['client_id'].count().reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### When we compare the 2 graphs below we can see that we have a large number of 18 year old customers but they don't spend much per customer, especially compared to the 42 year olds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<Figure size 1440x720 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,10))\n",
    "plt.subplot(2,1,1)\n",
    "sns.barplot(data=cp_age, x = 'age', y = 'client_id')\n",
    "plt.subplot(2,1,2)\n",
    "sns.barplot(data=cp_age_full, x = 'age', y = 'client_id')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4b: Client profile (gender)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex\n",
       "f    338402\n",
       "m    340930\n",
       "Name: client_id, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cp_sexe = full_data.groupby('sex')['client_id'].count()\n",
    "cp_sexe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = cp_sexe.plot.pie(figsize=(5, 5), autopct='%1.1f%%')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### These figures show how often a gender appears in our full dataframe and thus reflects the number of times someone of that gender has purchased something on our site."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4c: The customer profiles by the proportion of amount spent using a Lorenz curve and gini coefficient."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>client_id</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8151</th>\n",
       "      <td>c_8351</td>\n",
       "      <td>6.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7918</th>\n",
       "      <td>c_8140</td>\n",
       "      <td>8.30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7889</th>\n",
       "      <td>c_8114</td>\n",
       "      <td>9.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1556</th>\n",
       "      <td>c_240</td>\n",
       "      <td>11.06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4044</th>\n",
       "      <td>c_4648</td>\n",
       "      <td>11.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2513</th>\n",
       "      <td>c_3263</td>\n",
       "      <td>5276.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2724</th>\n",
       "      <td>c_3454</td>\n",
       "      <td>113669.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6337</th>\n",
       "      <td>c_6714</td>\n",
       "      <td>153662.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4388</th>\n",
       "      <td>c_4958</td>\n",
       "      <td>289760.34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>677</th>\n",
       "      <td>c_1609</td>\n",
       "      <td>324033.35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8600 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     client_id     price\n",
       "8151    c_8351      6.31\n",
       "7918    c_8140      8.30\n",
       "7889    c_8114      9.98\n",
       "1556     c_240     11.06\n",
       "4044    c_4648     11.20\n",
       "...        ...       ...\n",
       "2513    c_3263   5276.87\n",
       "2724    c_3454 113669.84\n",
       "6337    c_6714 153662.75\n",
       "4388    c_4958 289760.34\n",
       "677     c_1609 324033.35\n",
       "\n",
       "[8600 rows x 2 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding the amount each customer has spent\n",
    "ca_customer = full_data.groupby('client_id')['price'].sum().reset_index()\n",
    "\n",
    "# Sorting the result to be able to use it for a Lorenz curve\n",
    "ca_cust_sorted = ca_customer.sort_values('price')\n",
    "ca_cust_sorted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x16d3874d1c0>]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lorenz = ca_cust_sorted['price'].cumsum() / ca_cust_sorted['price'].sum()\n",
    "fig, ax = plt.subplots(figsize=[6,6])\n",
    "## scatter plot of Lorenz curve\n",
    "ax.scatter(np.arange(lorenz.size)/(lorenz.size-1), lorenz, color='r', s=10)\n",
    "## line plot of equality\n",
    "ax.plot([0,1], [0,1], color='g')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### As we can see from the graph our customers have unequal spending. 80% of customers contribute approximately 50% of the amount spent on our website."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.4463955226980713\n"
     ]
    }
   ],
   "source": [
    "# Calculating the gini coefficient\n",
    "array = ca_cust_sorted[\"price\"].to_numpy()\n",
    "def gini(array):\n",
    "    index = np.arange(1,array.shape[0]+1)\n",
    "    n = array.shape[0]\n",
    "    return ((np.sum((2 * index - n  - 1) * array)) / (n * np.sum(array)))\n",
    "print(gini(array))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The coefficient ranges from 0 to 1, with 0 representing perfect equality and 1 representing perfect inequality.\n",
    "#### The lower the value of the gini coefficient, the more equally customer spending is distributed."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5: The relationship between the gender of the customer and the category of book bought."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "categ    0.00    1.00   2.00\n",
      "sex                         \n",
      "f      206220  114899  17283\n",
      "m      209460  112270  19200\n",
      "The P-Value of the ChiSq Test is: 1.1989607410166063e-32\n"
     ]
    }
   ],
   "source": [
    "CrosstabResult=pd.crosstab(index=full_data['sex'],columns=full_data['categ'])\n",
    "print(CrosstabResult)\n",
    "ChiSqResult = stats.chi2_contingency(CrosstabResult)\n",
    "print('The P-Value of the ChiSq Test is:', (ChiSqResult[1]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Considering the p-value of 0.00 we can conclude that there's no significant relationship between the gender of the customer and the category of books bought"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6a: Correlation between age of customers and total amount spent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearsons correlation: -0.779 P-value is: 0.000\n"
     ]
    }
   ],
   "source": [
    "#Creating a dataframe with amount spend by age\n",
    "ca_age = full_data.groupby('age')['price'].sum().reset_index()\n",
    "\n",
    "#Calculating the correlation and the significance level\n",
    "corr, p_value = stats.pearsonr(ca_age['age'],ca_age['price'])\n",
    "print('Pearsons correlation: %.3f' % corr, 'P-value is: %.3f' % p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is a clear, negative correlation between the age of the customer and the amount spent, which means that our older customers spend more"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6b: Correlation between age of customers and frequency of purchase"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearsons correlation: -0.534 P-value is: 0.000\n"
     ]
    }
   ],
   "source": [
    "#Creating a dataframe with the number of sessions per age\n",
    "session_count = full_data.groupby('age')['session_id'].count().reset_index()\n",
    "\n",
    "#Calculating the correlation and the significance level\n",
    "corr, p_value = stats.pearsonr(session_count['age'],session_count['session_id'])\n",
    "print('Pearsons correlation: %.3f' % corr, 'P-value is: %.3f' % p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is a negative correlation between the age of the customer and the frequency of purchase, which means that older customers tend to purchase more often"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6c: Correlation between age of customers and average shopping basket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>age</th>\n",
       "      <th>client_id</th>\n",
       "      <th>price</th>\n",
       "      <th>avg basket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18</td>\n",
       "      <td>15149</td>\n",
       "      <td>609013.69</td>\n",
       "      <td>40.20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19</td>\n",
       "      <td>4371</td>\n",
       "      <td>178195.78</td>\n",
       "      <td>40.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>4587</td>\n",
       "      <td>184209.26</td>\n",
       "      <td>40.16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>21</td>\n",
       "      <td>4252</td>\n",
       "      <td>178497.70</td>\n",
       "      <td>41.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>22</td>\n",
       "      <td>4415</td>\n",
       "      <td>165029.26</td>\n",
       "      <td>37.38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>89</td>\n",
       "      <td>327</td>\n",
       "      <td>5264.99</td>\n",
       "      <td>16.10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>72</th>\n",
       "      <td>90</td>\n",
       "      <td>311</td>\n",
       "      <td>5043.19</td>\n",
       "      <td>16.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>91</td>\n",
       "      <td>170</td>\n",
       "      <td>2815.45</td>\n",
       "      <td>16.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>92</td>\n",
       "      <td>226</td>\n",
       "      <td>4013.87</td>\n",
       "      <td>17.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>93</td>\n",
       "      <td>201</td>\n",
       "      <td>3221.85</td>\n",
       "      <td>16.03</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>76 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  client_id     price  avg basket\n",
       "0    18      15149 609013.69       40.20\n",
       "1    19       4371 178195.78       40.77\n",
       "2    20       4587 184209.26       40.16\n",
       "3    21       4252 178497.70       41.98\n",
       "4    22       4415 165029.26       37.38\n",
       "..  ...        ...       ...         ...\n",
       "71   89        327   5264.99       16.10\n",
       "72   90        311   5043.19       16.22\n",
       "73   91        170   2815.45       16.56\n",
       "74   92        226   4013.87       17.76\n",
       "75   93        201   3221.85       16.03\n",
       "\n",
       "[76 rows x 4 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Finding the number of customers for each age\n",
    "age_count = full_data.groupby(['age'])['client_id'].count().reset_index()\n",
    "\n",
    "#Creating a dataframe with the number of customers and the amount spent for each age\n",
    "age_count_price = age_count.merge(ca_age, how = 'left')\n",
    "\n",
    "#Dividing the amount spent by the number of customers to obtain the average shopping basket per age\n",
    "age_count_price['avg basket'] = age_count_price['price']/age_count_price['client_id']\n",
    "age_count_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearsons correlation: -0.548 P-value is: 0.000\n"
     ]
    }
   ],
   "source": [
    "corr, p_value = stats.pearsonr(age_count_price['age'],age_count_price['avg basket'])\n",
    "print('Pearsons correlation: %.3f' % corr, 'P-value is: %.3f' % p_value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is a negative correlation between the age of the customer and the average shopping basket, which means that older customers tend to spend more per purchase"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6d: Correlation between age of client and book categories bought"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "F_onewayResult(statistic=45883.780588713154, pvalue=0.0)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.f_oneway(full_data['age'][full_data['categ'] == 0],\n",
    "               full_data['age'][full_data['categ'] == 1],\n",
    "               full_data['age'][full_data['categ'] == 2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### There is a statistically significant difference between the category of books our customers buy depending on age. This could be explained by the fact that the categories are related to price and as we have previously seen older people tend to buy more expensive (category 2) books.\n",
    "\n",
    "#### Further planned or post-hoc comparisons would need to be performed in order to tell which group is different"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 7: Probability of a client buying 0_525 if they've bought 2_159"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The probability of a customer buying both books is 86.6%\n"
     ]
    }
   ],
   "source": [
    "#find clients who've bought 2_159\n",
    "client_2159 = full_data.loc[full_data.id_prod == \"2_159\"]\n",
    "client_2159.drop(columns=['date', 'session_id','price', 'categ'], inplace=True)\n",
    "clients_2159 = client_2159.groupby(by=[\"client_id\"]).count().reset_index()\n",
    "\n",
    "#find clients who've bought 0_525\n",
    "client_0525 = full_data.loc[full_data.id_prod == \"0_525\"]\n",
    "client_0525.drop(columns=['date', 'session_id','price', 'categ'], inplace=True)\n",
    "clients_0525 = client_0525.groupby(by=[\"client_id\"]).count().reset_index()\n",
    "\n",
    "#find clients who've bought both\n",
    "clients_both = clients_2159.merge(clients_0525, on='client_id', how='inner')\n",
    "\n",
    "#calculate probability percentage\n",
    "print(f\"The probability of a customer buying both books is {len(clients_both)/len(clients_2159)*100:.1f}%\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Conclusions:\n",
    "1. Our Sales Figures are steady over time, with the exception of:\n",
    "    1. a dip in October 2021 due to category 1 books not being available for most of that month,\n",
    "    2. a slight rise in February and March 2022.\n",
    "\n",
    "<font color='blue'>Effort would need to be expended to create an increase in sales.</font>\n",
    "\n",
    "2. There is clear gender equality among our customers both in overall representation and spending.\n",
    "\n",
    "\n",
    "3. 82% of our sales are by customers under 55, with the biggest spenders being customers between the ages of 36 to 45.\n",
    "\n",
    "\n",
    "4. We have a high number of 18 year old customers, which could be due to that being the minimum age we allow. Customers under 18 who want to use our website most likely say they are 18 to gain access.\n",
    "\n",
    "\n",
    "5. Category 2 is our lowest earning category, which is likely due to the price of the books, however most of our top 10 earners are books in category 2, while all our top 10 lowest earners are books in category 0.\n",
    "\n",
    "\n",
    "6. There is unequality in the spending of our customers, with 80% of customers contributing only 50% of our earnings.\n",
    "\n",
    "\n",
    "7. There is no correlation between the gender of our customers and the category of books bought, but there's significant correlation between the age of our customers and the category of books bought.\n",
    "\n",
    "\n",
    "8. There is a negative correlation between the age of our customers and:\n",
    "    1. the amount spent\n",
    "    2. the average shopping basket\n",
    "    3. the frequency of visits resulting in a sale\n",
    "\n",
    "<font color='blue'>Our older customers spend more and visit more often.</font>"
   ]
  }
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