3.pandas_advanced.ipynb

{
 "cells": [
  {
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v74df00YO6mpRMFNqlYVaVuJ7+MtFLz3l/88vijwv1QchYXE4ZIxlO0WhuEWhiDF17PGGsxAA1KH9RFEeJHiasHUQyZZAkPe/DxozxoMtu2wUdKSX2EupDujjf+z+wn/OM2++HL0mHQ0mq2T5ncMYyzaIgEj1QeHz+L/x5YLff//YBfXO/QAA9NGDRWGeVV2HmsZrCk/C+cuGo777bzGvvxSo6+aCzST3+yUA0Ab3zf/pI7FVH4X/9Fr49YUQjaNLB1UF2+7so2SMMaedpKBonCzLNXOS72tfMiaNBqLjBUIEIlGYr/TqYW3b1bFHmpsQZUPQvOVK39fuSN/SqQfUsZKtSglQGFPP0SeMdl93SfDXL8eXb6D6IHrdIAQvS2WMdSYhwLapIaj0KfM+cKvn2osx4HVyvOMEwhT9nOHxxWt4mvAkhKBg2DV7WuDb9wmfpyuslzg9AoEApERVcc2abJw/Mfbusobn/5ZYvYlsG90uZ0Pgzj5Kxlg3gwgAFIqg6TJvuMT31S+qQ/qBpGR934kCodNtctLooFCS++fx6GiLFIXqGvTxI/N/+j9KYV4Or51vEwiAzgJ8iQJdl87Qp46NvLkk/NL8+JoKkBJNFwByOGSMdRBFgVic4glt5CDfQ7e5r7gAFJEsYkidq0+SEar9eivFBfaR6m59cj8BRaFQWBs5KP9njyilRd09Cqaly2SIhN/rueky87LzIu8sD/3uH/EPKwARPSYAOTOLjDHWXhBkXYNSmOd98DbPl69TCvOS2wY0PVEfPxAiAoAozFPHDrXeWIy6zhlhc4pCwbBSXpr/zDfVof3TWTZr5LSaI0Kfx7z+YtdF00J/fT3y93cSG7eCpqGhgcJzh4yxdoAAlg2S3JfO8H31dn38SKdVJCAe2xXyhIFQSvS49dFDI/96v3vOeZ2IIigUVooL8p56WBs9BCRHweNIpYYgpfB7fPfeZF49O/LagtAfX7U+/QyA0GuC5K2dGGNtBxFsKXxe3yN3mddfgi4jOR1znBG7E567CQBAHXSW8Hv5sr0JRVAoIooL8+Y96rpgMsjmiTZrLqMljdKjyHv3jcWvPRv4zgPaiIFUF6JoLNm/mzHGzpwiZF2DceFkz21XJdvCCHGCM8wJTz0CAUAb2l/p05NicT5PJSmCQhGltCj/2W+5Zk4G2dUXzrctp+qKSBTkee/5QtHLP837wX+oQ/vLYITCEUAERensQ2SM5ThbounSRg0BAKCTl26cPLapA/uoA3pTLN5NlwQ0oygUiojigvyfP+I6bwLYElrs18pOQDS2pBFF+Z47byh++Sd53/uqNqQfhaPUEEzW2vDbjTF2GgRSLK70KNbHjwKA1pyiT3iP1KZ6+ujBjWOs3ZmiUCgsigvyf/6ocf6kZBM1Pl+fnsyNf0sKvXfdUPSPZwKP3a+NHU6RKEViQMA9Sxljpw7BspVepdrwfs53J9WqbEafMR69Znffm1BVKBRWSgrzn/mW64LJyeqY7vwHaRPpjX+JRFGe9/6bi156Ov/Zb+sTR4JlU0MYgJKTi4wx1ioEAvUxw9DlauUDWhUItaEDlB5FZHXjjFARVB9SSgry533TNXMiV8e0MWcslABsKfL95nUXF734dN7T/61POZviCWoIJtf9cDhkjJ2UJFCEPmkUALSyz9dJFtQnVxN6TWPq2MS23d30PCQE1Ye0of0DT3/DmDiaV823F4EACJKAJHrc5o2Xui6aGn1vZeS1D6KL1kAkiroOugbSBl5qwRhrESJYtlKYrw0f1PoHteKELiUg6uNHYvfcQ0AICoW1UYPyf/UER8GOkF5HQSTyA+YNlxT85ruFL3zfmDUF3S5Z10CW5OyQMdYyBIontDHDlNKi1j/oZBlhijZqsNKrVFYfBVXtLmufEQFA1gddF07Ne+phtVcP7h3TcdLL8InQ0F0XTjXOnxRfvTH88lvRt5baVbXCbYCmOQsTO/tYGWNZAxHicWPaWPS6W5+3tOJOiACg9i3Xzh5K4Wh3WTMnBEkpg2Hzmovyn/mm2qsH947pBBmVpagIY9o5+U89XPDHH/ruuxnzAxQKUzgKitJd3pOMsRNDhIQlehRpZw85pce1LhDaEl2GPnYYCNEtpmeEgHgCE5bvnhvznnpYKSng6pjO1NiVhkDRjMlnB777UPE/n/F95VZ1cB95tJ4iMWe/TR4vZaxbE0jhqDqwjzZiEMApbArbuqFRZ8P6CaNFaaE8Wt/FN6xXBIVjwnT5vnWv945rkn1aOefIBqn9DgGFOriv/7H7PXfMjb6zPPT3txMbtwER6hroKncuZaybIgBEbeRg4feeUj1HKwMhAoA+ZqhaXhI/XANdeCcKRVB9SO3bK/CdB1xzZqT2rOIomDUw1SdCEgApvcs8d17vuvKC6AerIy+/Fd+4hY42gOlGTU1utsIY6yYQKWGJgNd13oRTfWhri2UAAL2mfs6I+MZt0CWHR4UAIqoL6tPG5n3nQW3MMC4QzWrJtRYSiJTSQs9Nc8yrZsaWrY/8493owlWy6ii6XKCrIAR0z2pnxrobBLAs7FGkjRmW+r61Wp0ROhvWz54a+tNrYFNX6ysmBMQTZFnmrVcGvnWvKMrnScHckFpoAQBoul0XTTNmTkxs2hZ5a2nkn+/Z+w+TZQuvG4Tg8VLGujokW+oTRylF+QCnFAdPJSMEAG3YAKVvubVrb9fZpxcBhKBgROQHfA/c6r3zOjR0zgVzTOZaC0XVx43Ux4303Hpl9LUPIu8sj6/7mBKW8LqTY6pd433LGGuGCBHdl0wHTT3Vc/ipBUL0mkppobXjM8AuMT4qBEhJ9SFt5ODAt+41Zk1OTixxFMxFyT5t5MzsqmeVex+8zfPFuZE3F0fmL4otX0/BMKoquAxA4P01GetSECGRUPr1Uof0c74/pUefWiCMr62If7QFDR1k7odBRVAkhgDuL8wJPHKX0rM0WRrDJfg5Lf0KEgEB+r3mTZe5r7kosXFL+OW3Y4vWWLs/BwD0epIXc5wgMtYFCKRI1DhnmDqgN8CpxsFWBkIiQKRgOPirv1FDCL2e3G7nIRAIqD6k9Cj0fe3fzZsvR13j4dCuJh0ObYm6qk8crU8cbe3cG/7Xe7EPVsfXbwbLRkMHTQGArnBhx1h3Jgk0XZs4GlX1NFqAtS4QSgIFwy/Njy1aix4zt6OgIiCWoHjCOG+C/9v36WcPBUlcGtNlOVveJ8dLhdqvl/+/viRvvzq2bF3kn+/FVmyQDSFARNMNALzigrGcJJAiMaW8xJg61vn2VJ+gFYFQEijC2nco+MIrAI279eYkRKoPieJ8//23eP5tLnpNIF4s3w00jpcCSBLFBe65s92Xz0xsqQy/9GZ0yYeJ7btQSjTdqRY2uXypx1i3g2Db2rAB2pB+rdx3qZlWBEIEAAj+5A+JzTtEwJer5wgEkJLCcWPmRP83vqyPH5XMEjgR7FYQAVOZn6ZqowYHRg/x7j0YfX9l9N1lsZUfUSQCQqBugCJ4vyfGcoBz5aqqrgunArR2A8JmThYIJYHA6LvLI//vbeHkT7kIEWwbTZf/kXvMW68UXjPVMoajYLeUri8lACKldw/PHXPNa2bHt1ZGXv0gtmCl9dl+iieE3wsCebyUsWxn2yLPb5x7zmk/wQkDIREItPcfavjRb2UkhqY7Z9NBpLil9C0yb75ceE2wbVCUzj4m1tmc7BBSCxD9XmPS2caks+0Dt0ZeWxB5d0VizSYKRdBlgKqAqvCSfMaykUCKxNxXzVL6lAGcQqPtJs9x3H9xKkVtu+GpF+LrN+dwFAQAIjQ0e8/+6NvLAFLNKhlzJMcGKNmwrUex9+4vFL7wZMELT3ruuVGUFJBlU30QEhYIwTPKjGUXIlAUY+YkNHSyZVsHQkkAEH1jUfilN9Hjzu1rYSJQVVnXEFu4CuCUl5iwbiEZDhGAwJbCa7pmTcn7zkNFrz+X973/MM6fhH5T1tZRLJ68J0dExjqdEBSJqYPO0s8eCgB4uqvAjzM0KiUoIvHJjvrvPw9SgrPMLqeRRNMVW1OR2LRNGz2Ey2RYyxAAEJRUhxpFqL17qHfMNW+5PL7u49j7K6MLVyc27wAJoCqoa9zUm7HOhACWrU8cpQ46C+j0m2C3FAiJQAhZW1f/+DNW5V705fjyeYckNHR75+fx9Zu10ae2eTHrjpIFNQCSAAF1zZgy1pg8xnPPjfFVmyL/fC++frN98AhYFnhMdJYq5vSoCWM5BxESFnrd7ounN95yWo4JhEQACEQNT/0+smBVDq+XOBYB6Grk9YXmjZei23V6Vbase8GMj5YtAUEpLnRfMdM957zE9t3Rd5bFlq2Lf/gJHa1HtwtUJVmE1WU+MoxlN0rY2oA+xpQxZ/g8LQVCgcHn/x761d9zeL3EcaCuJTZuTez4TB/FSSE7RYpIpn0EoAhtaD9tWH/vHXPj6zfHVm6IzF9s7d4PkSgoKppGeq/Ezj5oxro023Zdci4GvGeY2DQNhFKCEJFXF9T/4HkwNGev2jM90OxBBIoiG8LR1xdxIGSnw/mkJZfkA5DEgM+YOcmYOcl7/y3RJR9GX1+Y2LQtsWMPEAnTDQomS7O60ueIsWyACNIpapsKkOwDetpPlhEIJYEQ0Q9WH33kJxRPoK53wREeRJAyunit976bRJ6fR0fZaUquQXRqaggEioDPvPx884qZ1o490UVrYovWxJ1GpgJBVVHXAIFXIrIs5ZRAn+7ag84hkOrDrsvP10YMdL49kydLBUJbgiISm7bVPfoTeaQWPbm8avAEiNBt2Nt2xZatc18+kwMhO1OIyetQJ8IRqQP7eAf2MW+4xNqxJ756Y+SNRYntu6m2DgDR606Nr3KOyLKAUxGGAOEoScqx0z4BqKo+dSyartPYbqIZFSAVBT/eXnPvE1blHvR6umxFOBGoql19NL50vfvymRwFWZtJv5eIgEj4vfo5w/Vzhnu+dG1ibUX0g9WxDZ8kPvyE6kNg6Oi0qgEEktzOlHU0J/5JCQmLEnGySRs5UOQFEus358zqWCEoHFH7lbvnzAA403QQAJJbN8U3bq297zvW9l3o67pR0CGl8LqjC1Z6tu1Wh/TlBYWsjTU2MiVAREPXp52jnztO1jXE11bEl66LLlxj7d5HoagzPgGqymU1rCMgAApAoGgcEgnQNOH3uKad67p0hj5maOjPr8eXrUO/N2eSQkn6xNFKn55t8mQqKCK29MOj33jK2r4b/d4uHgUhmRRae/bHPqxQh/TlNjOsXTRu/EQAAFIKv9d14VTXBZO9998S37g19t6K+IcfW7s+t2uOosuFLr3x/hwUWRsSmO6XJMMhtG2lVw916AD37CnGjAlK7x7ocVt7D0TeWAiGDrkyQGHb6DLcc2cDnOZ2E82owd/9o+Gnf5AHq9Df1XPBNERAjLz6gXnTZcjpIGtXyULTVKsaIURJgevCKa7ZU2XV0fiHFbGVH8WXrY9vqQTLAiFQU0FVubKGnSmBIATYkuIJsGywbQz4XLOn6ZNHu84dp40ajLoGkFzzGluyVu4/gqqSG3FQIIVi+rnn6BNHtdVTqvXf/jkBoscEO0cy4jahKImK7YmN2/Sxw7hkhnWEdKuaZHgjUZTnumS6cdE0eaTG+mRHdMGq2LJ11t6Dsq4BBaLbBU7DGuDKGtY6mPw/QKR4nEIRNHSRn6cNOcu46Fx9yhht0FnC5wFI7lDt3BMAom8upUQCjVxZKYCUSJhXzxJe88zLZBwqKApiN+uFQYSGLqtqI28u4UDIOhQ2LauREhGV0iKltMiYOUnW1sdWfRRbtCZRsT2xbZesrUdDR00FoYDi9HvrTp9T1kqIyY0zpSTbhliCbFs9q6c2tL8+boRxwWR12ADhNpJ3tiUggBDJehkU8Q1bEhXbQFNzY1xUCIpEteEDjXPHAZx+T7Vm1OSgTXdE8eXr7eqjSmEex0LWCRBBUQAoeW0uUBQE3HPOc885z66qSWzZlVizKbpwdWLrTgpFIJEATUWXC0SqZ033/AHEiVQAACAASURBVNQyh5P8OcMM8YSzKQrqmlKYb0wdo08bp589RB3cFw09eX9nKAKhSf5EAADxRavtzw9hni83BgURKGHpE0ep/Xu3Yamj2gZRsDEfh5xZICUlesz4pm3xZevdV13AgZB1HkxthuasLwQAUooKlOkFrunjvHffaO/ZH128Nrp4rb1jj/XZfoon0OtGVU2uAMuVTxxrE+kXHQBsmyIxsCxRWqQOOksbNdh1yTRt9FCltAg1FSA55ACAyXGIZqc4IlCEPFITeWc5aG0RCDqAs8V6QcCce2HbPvEJd6hv6UAAU8M7TvyzJUgJ0iZbAhFqas6sRhAIkWhs4SrXnBnovA84FrLOlPpwZTSsQa+pDh/oHT7Qe/cXElt3xlasT2zYGlu90f5sH6AARFAF6hqgAJLJzJJ1PQKdl5gSFlh2clA9z++aPk4fO1wdPdQYP0IU5jXe38ntnHqZ4yECxPjGbfGK7eh25cabBxESCW3y2fr4kclv24ja2D6xxYUEmPEVSbAlWJIsCySRZYGUaOjo0sF0K24DDIMaghQM50YslAQuPbpglXffIbVvORCvpGBZI7NhjfPOFKgNH6ANH0DxhLV7n125J7psfWz5enngiDxaT4mEcLnA0JMTP8BpYo5LFxsjgCSKxikaQ00VeT5RVKCPH2FMH68O7KMN6IN+b+O5K/2in7R+hAiEACmjby2BRAJ0LWfeMASemy9H0922qYsKlgUAYEvK/EPIVMMLaTv7sZEl0dAx4BVejyjwC58H8/yiIKCWFYuyYvWscvWsnqJXafDp39f94NfCnxObNxGqql1VG31vuffOGzgKsmyUHHppbOGGuqYN7qsN7uu6dAbF4omN22JLP4xv2pbYvMPeuY9Aoq6jqoAQoIjkwGmunOO6uSYNbCXYkiyLYgl06eqgs9R+vbQRg4ypY7UxQ5OVn47kmRaTU4anwj5QFV20BoTShr9EOxJI4Yg+Zqhx7jlt/twqGDrYUjmrVPg9YEnnAkSU5In8PJBSKS1GvwcIlJ5F4DGF3yv8HlGQJ/J8wudtct0hJQjhunRG6K9v2Ieq0dCzPdcmACEgkYi+ucR75w08LsqyWpP1iOQU/qGh6xNH6RNHUcJKbNtlbd4R37Altny9vXMfJRIQSoBQ0KWDqqY2kMruj2T35NR8AkDCpngCbBtUBVUV83zGyMHauBHa0H7aiIHKWeWoZkQsJ1c58eDnCRABYmT+InmoCg0NKPvzluQ2vK45M0RJIdh2cu/PNqKWvPtbIEDThZrWeNXp1kHXgQhdxnEjRHImNnWIiGBLbeRgY+o54Rfng8vIgWJcIjQMa/OO2PL1xrRzuN0aywHpUVPISBNVRR85SB85yD13tqw5au3eH1u+PrG2wtpzwN53UNYcRVVDlwaqAigaH8txseM1KVohIKJ4HKJxklIU5qk9i5WeJdrZQ4yJZ6tD+4nCPBHwpe5LIGXj9dCZnKmIQAiKJ2ILV1Moivn+HGilIgSFo8qAXu4rZjrftu3Tq+qAPie5S7JHFDUOHqZfyGZHgwQAnpsuj76zXEajqGZ9JRIRGLp1sDr6wSpjWtun24y1r8w0kcipVkuuSpw0GgDs/YfjaysSn+yIb9xqbd1pH6oCScnTqCKSpW2Sk8V2lsz5EKQEyybbTlY2EaGqakP6qYP6qIP76SMGqUP6qv16NTmvJl8aTC7+axOSQMH42or4hx+jN0d2nCACKV0zJ6sD+rRHYaPaJKtrQer6pTV7HiICgDZuhD5hZPSDVaDlwgSslMJ0xRause+4VulVyrWjLCc1i4ipW5SeJe6rZrmvmiWPNtgHjtifH4iv3xxbU2Hv2EPhiF0fhFgcNQ0NDTQNoLHrTfaP5mS1zFIXIohbFIuTbaOhC49bmH7Ro0gbPVQ/e4g6qI9aXipKi5ov+Eu/iMcuezhzAgEgumCVXVUr8vw5EAgRwbbR5zFvnAPQNs1Fm2nT1Q6IIAndhnnL5dEPVufA0Cgkk8JExfb46o/cvS7m2lGW25qdN1MTiiLPJ/J82rD+rgunUjROtXWJLZWxdZ9Y23fLfYcTew7Ig0fIlqBrQlNBERmnYASQvFrxuDC1Ss9ZA0oSiMiyk5lfIoGapvTqofbuoZSXqoPO0oYN0EYOwoKAcBtNZrmchi/pepn2uxwnAkT7cHX0rSXoduXGyyqQGqLuy8/XRg4CaMtVE2mnuo7wZBAAwJgyVh8/PL7mY/SauXG5gRh+ZYHr8plo6JwUsq6j2TIMIEBEt4HuEqNniTFrChDJqlpr9z7r84PWrs8TG7clKj6V1UfBtihhg20DAqhqcgNFFKlV/92yA2o6z4P0wobUwj5nbZ/zh0IUhQFt4FlK33Jt+ECltEjtXar27YV5vuZPmNqrC6AVCx7aVHzlBvuz/Zgr6+htGz1u88pZyW6o7VDJ0eaBEMGWojDPvPGy+LotuVGMRASqkli/2dpcqY0dxkkh64IyS/PTQREAEEVxgV5coE8YBQAyHKWjDbK61tqxO17xqb1rn7Vnv6wL0tF6WRekWNyJiKiqoKnHv17M5ZHVzF8Km45qEZEtwbLAtilhgZTocikFAQh4hd+jlBarA3prw/orvcrU/r3Q60bTjWrGCVZS6ulSS2LaY9jzxJy4KynyrwVk2bnRZVsRVB9yXzLdmDkRoF3SQWj7QAjJAWj3FTNDL/wz8fEO9GT9ZCwR6pp9pCby1hJt7DDgSMi6sGMbaKRXVqAQpgtMl9KzWBs12D33IgCgugZr70F7517r80PW/sNy/yH7SK29/7Dcf5jiiWQekxpHRSEABSipYVWR6mPsxF1K/bhOl16dCemhyNS+V9IpY5EgiUg2HjlJQCHyA0qPIlFaqJSViAK/UlqkDuij9O6h9ixFv6eFH2RLJwtvrJfpXE43mQ2b4+s3g6pkxWtxYohg2eg2XFfNQpfRfoX97RAIEYFI5PvdV85KfFKZ7asJHQLBltFFa7x33SCK8nl0lHUjmXlJOm6ldsnAgE8L+JJzMwAUT8i6BllTR7X18nC19dl+e/9h+8Bha+8hWVUDcUvG45BIUMwGy6JEAhUFhABVcUpVUQgQmJobS50ZTnCGaOWZ+gSfVjzmK8sGKUnaICXYqR0bJKGugqqiroFLE5oGuir8PqV3D1FeopWVKD2LRVkx5ueJPJ8IeBtXNZzogLGDBzxPymm7HV242tp7QOQHcmDVBADF4trZQ92XngvQXukgtEsghOQHybzuovBL8609+3Ngcb0t0eeJb9waXb7evGoWSNm2qzUZyw2N+SI0Dp+msyIE1DWluEApLmjyKNsmW1JDyD5YZe87ZFfVyMO1suaoPFwjg2EKR+TReoonqC5EsRhFYrKmBhSB6ZOaoqSGCkWT6nQCVJRWnPuImp3QbWqclJHJ5c7kJHxCiIAPXbri94JpCNONHhO9pvCaSkkBFuQrPQqV4gJRUiBKCoTPhwpCi8eQbmsAGX+xdi1yOXNEqAj7YFVswaqcmR0EAoHeO69Dr6dd13m3TyAUCERKrx6uq2c1/PA36HIBZP2lByJaMrZgpXn5TFAUHh9l3V2Ts3zzJfyZWSMoCioKFuaJwjxtxMAmT2LZFInaVbUUi1NtPYVjMhyWNXUkpTxcDbYEAvvgEQpHQFWopsGuqs383NmHq6khlGy80iIpweXSepU1ntaJRElBclUAkSgIiIIAEIiCALpdgKgUBNDtwjwfetzC5xV5XnTpJ2ozll7PQNA4jprZ1iBXNI6LfoJuVw4UcAhBobA2cpA+1Vnk3Y5/8PYJhJBKCq+aFfn7O/b+w+jK+qSQCN1G9P1V1u596oDePFPIWAsyKyczpdPHZlQFfR7V19L8Wfqh8QRYNiBQPEGReOPHjoAi0WQz5OM+mEBR0Gs2OUa3gaku0qhrJx/dOW4/gYz9QLI51WslIQAg+trC5NRPTmSEBO6rZqnlpe3d9qvdAqFAkFIbNsB9xQXBeX/OgY5rRKAqsuZodP4i74O3JW/pAu9+xjrA8bavocyJwKaTgqk8D3UNdA0A0O2CQDscW7o3VuOhQmPpZvK7rv5JJwJEa9e+6NIPQc2FeR8hKBzVhg9w33ApQLu/QO05l4sIAJ4vXqWUlzobKLfjz2oTAsG2I++toGgsB46WseyHGRWkQiT/U0STNfvpslVn4q3Jf7J1/zV9FDX9z/kpSurnJg8DGw+gO3zYCQAg/NoCqjmKup4D6SBJADJvuFQtK072BWxP7RwIidR+vczrL6ZYLAcGGiWhy0hs2RFduBogO+q8GevyMgOSaPafaN1/TR+F3SzInRQRCKT6YPyDVRRPnGjONUsgUtxSB55lXn8JQEdMUrVzdS8RAJjXX6r2KadY1r8ARKBpsupo7L0VAAAosnw0lzHGTo4IAGIrNsQ3bEGPmdy/PpshQDzhue1KUVLQuOdGe2rnQCgESFKH9HVfPYsiscYtYLKWlMJjRhetSWzdCQg5UFjFGGMn4Gy6ZMvoB6tlMJxtSxtbIASFotqIQa7LzgOAjilabP8/CgIAmF+4VO1XTpFotm/4RwSaau3ZH1+xobMPhTHGzhgRANi7P0922c7y6n1IHrB77oVq7zKQsmPGETsgECJIqQ3p77n9KrBlTky8oaqGX36LYgkQPDrKGMtpCACR+YvtIzWoKtlevS8ERWPamKGe264E6Lhq3g7Jz5zy0ZsuVwf2oXgOlI+iqlrbP4suXgMA2f6+aQcyhXLhqoWxEyOibv2WFkjRePSdZWDLXLiyJ5Dk/dK1ojCvA4pF0zoqEEoSpYWeW66AWCLbAyER6Kqsa4i+tRSgG8ZBECmISES2bcssb5vO2DHS8Q8RM9/SnX1cHUtKAIguWp3YshNdWb9qQggKRfVxw10XTQPo0I4mHTpj575ipjZqMIUjOTBTqGux5eus7TtBYA6MqrepjRs/WrRo0Y4dO6qqqohIURQhBABQSmcfIGPHlX6XpuNfQ0PDvn2ff/jh2kWLFoVCIec+nX2YHQURAKLvrZA1daBpWR0InW3oddXz79eJggDYHVEsmtZunWWaEQhSKn3KPF+ce/TRn2T7xkyS0G1YO/ZEF671Du7f2UfTlmzbhuTCLQEAmRfIzrlj69at1113bVVVlcfjGTZs6OjRY4YNGz5hwoRRo0aKVD9GJ0EUWX41w7qTdPBzhjEQsaqqasWKFZs3f7xmzdrt27cdOXIkFov98If/d/fd9xz72PQzAEDXSRwlgcDE5srYgpXCm/Xb4SFSOOqaPdV99SwA6OBkqaMCISSvTcxrZof/9mZ8w2Y0s/uFIUBNjcxfbF5/iSgIZHO7NWcICFNOfGclo++iM+zZ7FGqqhCR2+1GxE2bKlavXiOE0q9fvyFDBl9wwazZsy/q3bu3qqqQCoctXlynn/DYLxhrpfRb69gvMjmRz0n+iKi2tnb9+vWvvPLPLVu2bN++vba2xjBcuq7rui6ljMXimc+fmTu2/i3a7IFn9lu2JwQAiK5Yb+87hF4zq0e2EMGWYLrM265EVW3vzqLH6thAKCUGvL4Hbqm589tZnaRDcrfexEdbEpu2GedPzOZAiIiZ4S0dFDPv43xoo9Hohg0bGhoaTNPs1au8vLyXpmnp+9i2rShKWVnPMWPGLF68WNd1wzDcbjcR7dv3+a5dOxctWjxv3rzp06fPmTNn/PgJpaWlp3ScnEey1sjM7ZxbThyinCgYjUY3bdq4bNnS1157befOncFgEABM0ywsLHLCp5TS7XZPnjwJUm/1zB9RVVX1+ed7q6urpZSjR59dWlrqHENLByZac8XZ+YgAUdYHo/94F4QCgFld7yAE1dW7r7vIdcFkgE5o/dqBgRCSv54xe5px4ZTI/EUi4MveHgdEoCoUjIT+Ot84f2J2RkHns7pnz56lS5eUl5ePHDmyqKjYiTTNoo5zz127dl599ZWhUMjn8/fv33/gwIHDh4+YNm3a2LFjPB6voihSSo/Hc9NNNy9atMgpsXOGUl0ul2matm0fOXLoxRf/+vLLfxs5cuSFF86eNGlyKBQ8fPhwZngjotLSUq/X6/F4evXqXVRUpKqqc8EOqfy164w+sTZy7MiklNK27bq6uiNHjhw8eNC2rT179kppp985Ukqfz1dSUrJz586FCxcuX768trbG5XKpqurz+RDReQYAUBSloaHh6qvnjhgx0vkWES3L3rmzcsmSxR99tGHXrl07duw4cuRIXV3dvHnP3nvvfc0CYbroBgCi0eiWLVs2b948aNDAiRMndfzfqlWcTZdWrE9s3QVZvvsgIsTjokeR954vtOs29CfQ4YFQStQ1753Xx1d9JMNRVLP7FVJEfP3Hia07taH92y8pzKxAcT57rQwSzmf1D3944Uc/+r/y8nK/PzBt2tRLLpkzYcKE/Pz8zCFT5/5ut9mnT58DBw4IoezY8WlFxabXX3+tuLi4Z8+y8847f/bsiwYMGNijR4+rr5776quvvvrqK/n5+c55JD2Iqmm6rhsAUFFRsXHjxsLCwkQiEYlEms01ejweVVVVVfV6fS6X0b//gFGjRo0ePWrIkGG9evVSFMV5Que0whGxm2s2zFhdXb13796NGz9atWpVZWVlQ0N9OByJRMJE1NDQ0OyBmqaZpllXVxeLxbxeb35+vnMJmFnn7OSLJSUl99//FY/HEwqFKisrV6xY/s4771RW7jh8+HAoFDIMwzCM/Px8APD7/c2OzXmSWCxWWVn59ttvvvXW2wcPHjx8+NCoUaN+97sX+vTp41zbteY3zfwi9dFsn/c/IgBEXltIDUH0ebN6HgqRojHz9qv1cSM6JQpCRwdCABACiIzp412XzQz/+dWsLmSShG6XtWtf5O1l7RcIm40CpW+Wkk4aFJ2Pk6IoUtrBYPDIkSOVlTtefPHFcePGXXvtdVddNbegIBkOnTv37dt3+vQZv/nNrwsLCw3DcLtNIllfX19dXf3xx588//zzo0ePnjXrwjlzLnvooYc2bdp48OBBXdebfXqd//V4PAAQCoUQUdf1ZgcWi8UikQgRVVUdsSx78+bNb745HxH69x8wc+asadOmTpt2bl5eHmScBNvq78lySGYItCxr48aPFi9evHjx4oqKTcFgUEqS0hZCOHXLiJg5kp9+hoaGBl3X3W53Ov9rxhnbuPPOu/r37/fCCy+sWLF84cIPamtrAUAIYRhGYWERkUTEUCjUr1+/cePGpR/oHJuUctWqlS+99NJbb71ZXV1NRIZhxONxInC73dCKwdt0NHVuSd+fiNrlvS8lCJGo2B5bth50PasHRREpFlP79PR+6Rrn2045ig4PhJDc8tZ71w2xRavtQ9XpLTSzEyoi9v4KecsVoji/PWIhIsZisd27d9fX1xcVFeXn57vdbsMwhEBoWgjT4mMBYObMC/7whz80NNR7vT4pbSJauXLlqlWr/vznP91yyy3XXntdIJDnZGCqql500ey///1v8XhcURTbtgBA0zQn2kkpN2zYsGLFit/97rcTJkzMz88/fPjw8QrNneDqVM00i2TOigtn2jJdxefYtWvXJ5/8/E9/+sOIESMuv/yKG2+8sbi4BDIukNv2b8uyWWamtXz5sr/85c8rV67ct2+fpmlOftZsvB1aeqela2RaDIEAgIiWZeXn52/evPnaa6/ZuHETALndbtM0MwZgLQBQFCUSicyYcd7QocMsy1IUgSgSifjixYv/8pe/LFu29PDhwx6P13mgk2Wee+65xcXFx0sHMyc7nZ9l23YoFIrH4wcOHLAsq0+f3oWFRW35N20qumi1tfeAyPdn7wwUACBAwjJvu1IdeFYnlmJ0RiAUCETasP7mbVc2fP9X4NLBztZAKCWa7vj6zfF1H7sumd62r5PzIampqfn61/9rwYIFmqYrCubl5Q8dOmT06DHDh48YMmRInz5nKUrjGj5oWmzifLqmTJkya9asF1/8azp78/l8RPTRRx9VVFT88Y9//Ld/u+Oqq652BktnzZo9c+YFb7zxhnOfZs/sdrs9Hk99ff3rr7/m8/lUVT1eIEz/Cs2+OPHtuq67XC7bttesWbNu3bq//vWvt9566/XX3+CcTZzz2pn+WVnWc95yTha4YsXy3/72N4sWLQwGQ7quO+9SR4ttHI73TjvBz3Ii3BtvvE5Efn9y7jBzPgJS16Pl5eW33347ACiKYlnW2rUrf/3rX73//vvBYIPbbebnFzgXmogYiUT69Olz8803t/QTJRFkhD+srq7evn3bjh071q1bV1Gx6cCBA5Zl27YVCAS++93vXXbZZW08KEIEQsiausibS9DQs7pYVBGyIayPGWZ+4bLOPZDOCISQTArNL1wW+dcCa/uurF5KgQiWFf7bW8bMSWjobX7N8tFHG9566y3LslQ1LqWsqamtrKx89dXXdF0fNmz4yJEjx40bN3v27F69ejsflcxik/SSqQcffNC5YjUMIz1A5PP5bNv+5JNP/vM//+OVV/51++1fnDPnMpfLdd999y9ZsjgeTzSLc+mzj6qqeXl5tm2f9CxzqtJzjX6/37btysrKRx99ZP78+V/96n9ceOGFzhmKY2HXli7j2rFjx7PPPvuvf/2joaHe5XJ7vV5IrXNtc7Ztu91uIcQJEsdoNHLttV8eO/YcANi8efOvfvXc/Pnza2pq3G53IJCXThwBgIgsy7rzzruGDBmajmFNL1UJEY8ePfrhh2tXrVq1bt2HFRUVVVVVTtUYplb6V1VVvfrqK3PmzEl/kNvwV46t+iixYUvylJWdEMCWwtA9d9+olBV31uygQ3n88cc74ccigi1FwCu8Zuz9lQCUvb1mEIBAVtUasyYrJYVAbTmK7bz1//WvfyYSCU3TFEXRNM0ZGlIU5cCBAx9++OHSpUvfeOP1pUuXAkBeXr5pmk7NW2bWVVxc4vV63333HWdBVPp2AHBWQXz66afvvvvuhg3rS0pKp00717Ls999/3zTN44W6Ng+BLT6/85vu2rVr/vz5lZU7hg8fUVBQ4JwoeZi060nHiWg0+sILL3zta/+5ZMkiIRSXy50eP2+nH52OVS3+qxAiGAyOHj36qaeerq+v+/GPf/TEE48vWbLEGSNxrs/Sd1YUJRgMXnzxJY899nizOUsnyNXV1W3YsOE3v/n1d7/7xIsvvrRgwft79uwBANM0dV3XNM0Jh06R9vXXXz958uS2nBpwrtQl1X/veWv77qwOhEJQQ8h9ybmBR+5OnlQ771PfSYEQnO4moA3rH6/Ynthcmb0vGAGqCjUEhd/rOm9CG2aEzvs+Ly9/+/bta9eucbncRDI9LgQAuq47NSm1tbWffvrpG2+88eabb1ZW7pDSdpYApj/eiDhq1OjPP//8ww/XOCv/Gg+fiIicKf3t27e/9tqrhw4dmj37os8+27179+7MWpiO5xybYRhEcv369QsWLCgsLBwyZLBzjuBY2JWkh0O3b9/+9a9/7fnnnwuFQh6PN/OSrlMgom3bfr//61//+q5dux944Ctvv/12PJ7w+Vo4NiFEOBwuLy+fN+/ZsrIyy7LSYzOI+Mknn7z00otPPvn9efN+tnr1aqdDocfjMQwjc6bc+TvE4/GysrJvfvNbBQUFAIRttVcrESDGVm8M/epvFEtg1u4+iAiWJfL8eT/4L6W81GmC04mH04mBMFnapJQURd9aAok4COXkj+oUioBYXDaE3JedJ7yeNoyFUkohsG/fsxYtWlhbW6tpWrMY5lyKpi8kjx6tXb161fvvv79q1aqamuqysh5+v98pFhBCjBw5au3atbt3724WCyEVLHVdt217+fLlH3/8sWEYVVVVbfJbnCHn4N1u95Ejh9955536+oaJEyc6Y7zAqWGXkD77v/LKKw899MCaNas9Ho+madnQzN0JhGVlZXv37n3uuefq6+u9Xo+iiGOPzbmnEOKHP/zRjBkzLMtSVRURg8HgwoULn376x88+O+8f//jHwYMHdV1zatCcitNmH0YnajY0NDz88MMXX3xpG08HEAFi6Lf/iL6zXPiyuJuMEBSKeO64xnNrh263dDydFwgBnGFHtU+ZrKmLL9+AbiNbk0JCXZeHqrXhA7URA9s2KZRSlpSUBAJ577zzNhyn8Ur6g6SqqmmalpXYvn37smXLXn311d27d5umWVRUpOtaIBAYM2bse++9V1dXq2ktpHqpkGMePLj/8OFDqqp17sV4JiLSdR2Ali1bumnTxokTJ+bnF7T5xAnreM6JPpFIPPPMz7/97W9VVR1xCrWy572nKEp9fX1lZaXb7XY6sR17H+d9GA6Hv/GNb/z7v3/Z+ShVVla+/PLfHn30kT/84YX169cHg0Gfz6dpGsCJRnoVRamrq7vyyisfe+wJp+66zd7kUoIQ9t4DDT/+rawLZu8qbSEoFNFGDgp890ER8HXkdkvH06mB0Gn6g6gN6B1bss4+WJW9SymEgFgcojH3pTPavGQGEUeOHBkKhZYuXaLr+knXJCGiM3XR0NCwevXqV1751/r168PhcFlZ2YABA0aMGDF//vxIJNIsv8x8BuenZM+ZyJFODT/55JPFixf179+/f/8BnBfmNCltIZTq6uqHH/76888/pyiKUzbc2cfVAsMwjhe9nAvWYDB4zz33futbj0UikRUrlj/33HNPPvn9//f//l5bW+t8JFVVPemWh06Pm/Hjxz/99E9PsPTiNBEBYmT+4vCfXkPTnaXnUmc2UErf177kOm9i59bIpHVuRpjsNSPy/KBpsQ9Wdfp1wXERoarYB464Lpis9CwBaLNcPl0wNnHixOrqqnXr1hmG0YrDSS6ld7lcALB165aFCxcuXLigpqb6ggsu6Nu3//LlS2OxeGYP0mMfnp2IyDTNgwcPfvDBwpKS4lGjRhFJZzypsw+NnRqnpefOnZX333/fG2+87vV6mxWeZJUTfCgQMR6Pz50797//+39WrFjx6KP/8+tf/3rx4sXRQHaz1wAAIABJREFUaMTpTehMT5z0Y6UoSjgc7tWr1zPPPDt8+PC2j4JCUDRW/51f2PsOodpJKwJOShHUEHLNnur7n7tRUwE6f1wUOj8QOhC1EQMTH2+3Pv40ewdIFYWCYUB0X3wuILbtAKmTpU2fPn3Llm0VFZuc2pbWyEwQDxw4uHjx4jffnF9SUqLrrr1797TJ4XU8p4ImHA6/++67gYB/3LgJzu0cC3OIlFJRlPXr191//32rVq0MBAKQ3Vdgx+MsMRw4cOCMGTN++ctf/OxnP921a5dt216vVwil9b+REwWLi4ufffaXU6ZMaZeVQojxlR+FnnsJCLMhzWoBIiRske/P+8HX1L7lWZIOQlYEQqcBqaKo/cpj76+U9UHMzr5riGDbFAy7Zk0R+f7kLU2ll+KlLw+JyOlvdOKTuHOlbBjGpEmTtm/fvnnzZqfSrJWHlrlSoqamZsWKZeFw2FlQmKPBg4icgaYPPvggHo9PnTrV6VCao79Od+PkgitWLL/vvnu3bdsWCASyNhFsDedd99577+3cudNp/OQUNrf+GZxy09LS0mef/cV5553v/H1O/JD0CaTZKQVOcDJBbPjZH+OrN6HHlaVlMkLIUNh33y3mjZc6KWxnH1BSFgRCcGIhKWXFFI7Elq5DXc3G3nhEqGvywGGlpNCYMqbFel+nHkykYIbM4mlo6a3sxMJAIDB9+oyDBw9+/HFFeoFEqw+QiEhVNcMwQqFQrocNInKC38KFC2Ox6PTpMxRFOD07OvvQ2Ik4Z/lVq1Y+8MBXdu/+zOv15HQUBABEDIfDmqa5XK7MT3ErKYoSDod69CibN+/Z88+feYIomBnwMs8ezU4pLT2SQGD84+3Bn/2JwhFUlWw8hQpBwZAxbmTe/z6EHjdAVgyKOrJmHBkBALxfvj76war4uk+ytNcMAgDGPljl+eLVzXbrpdSGf++883ZFRYVzc2FhUUlJaf/+/QcMGODz+SAj/qVrQDLf1s5MQ1lZ2TPPPOvz+V544fc+n8+pwG79MTodnk7aHS0nOMNrgYB/3rx5iPjYY48hilwP8F2b85KtXbvmwQcf2L17t8eT81HQ4XygTuN3cRbg9+/f/6mnfjJ9+nTn75N5h8zFhZmDpZZl7d27d8uWLYcPHzpw4ICUtpRUUlJy9dVzW9guEQEAYu+vsnftw4AnG5uLIoJto8f0PnCrKM7v9IWDzWRPIESQEvN8/v+6o/rL3wTbdvap6OzDasqW6DVjaypiKze4Lzv/2EC4atWqe+65u66uzrnRNE2Px5OXl+fz+fr27Td58uTx4yf06dO7qKg4vWNDs+EOJy80TfeTTz7p8Zi/+c1vnHYzp/oJ7AJR0OH8YT0e85lnfi6E+Pa3H0Ns3nyZZQln3mvFiuUPPfTArl27vV5vdhaInobT+0AJIY4ePXrOOeOeeWbeiBEjMucFM58wvVVndXXVvn37KyoqVq1auXXrlqNHa6ura0KhUDAYpNQ2HR9/XPH00z91asKTnwJJINA+WBV5bQFoStadNh1CUDBk3nqVa84MAMiqKAhZFAghtW3vrCnmtReHfv9P9Huz9BUlGX7pTdfF5yaX6WSckZ02acXFxc6YnjNLd+jQoc8//3zLli3vv/8eIvbq1WvSpMljx44dMWLEiBEjnUwRMpqIOnmh221+73tPFheX/vjH/+c0YOuUK+tsWGXhfOBN03zuuV+UlZXdddfdzqY5nXtUrBnn3bty5YoHH3xg9+7PWhMFs+Hd1U6c92ddXd2FF170/e9/f/Dgwc6IKB2zMbVty08/3bp58+aKiorly5dv27YlGo05nX6di2DDMEzTBABFUWpqarxe3zHbUREAxlZsSGzZibqWpYOioYg6oI/vodtQiGxLByFb5ggdqVJMdXDf2NJ18mBVNvZdQ0REebjGmDFBKSt2bkn/S3l5eW1tzdKlS/8/e9cdH0XR/mdm9+72+l16r0DoHaUjoAKKAgoWsPFTEbEXwE4RAVFEBAX1FXlFAREFGwqICkgVpPcOIT0k12/bzO+PSfY9Q3K5hORygfv+wee47M2WmX2+zzxVo9HQpUyLCmo0Glpmgpbi3bNnz6+//rpx458bN248evQoQigiIlI5QGkfCADo1q1bfHz8pk0b3W43zXMKzl0qTeCoJYcacxpWZlGteePGP81mc6dOncM12EIKVLhnZ2ePGzf22LFjtOB7VQdDCOmior8KhdVVt6Br1eVyjRw5as6cOUlJSTRsTWlwCCEUReHIkcOrVq1asODDzz77z5dfLtm0aRMt9sSyLA18U6IE6IMqLS1t06bNtGlvWSxWQvD/qrJR0n1jnnw6G3KhF3VPBTsA5jee1PTqBGQZhF7ht1AiQlAWNYOsJiYqwrtuCwANX3GgErAssTmAmtXe1F2pwU11W4RQjx49T506tWvXTlom1Df0i3oEWZblOE6r1TqdzpMnT+7a9feaNWtWr16dk3OREKLXG2gZfvouYYzbtm3Xrl27PXv2ZGdnU199vd4cvUie5+12m8fjUavVdrvd5XJCiGhBqXo9u/8LYxhGluVNmzYlJSW2bt0mzIUhAiqmc3JyHn/8sd27d5tMJj/WCwihKIpOp5M21PV6vTabjQYJ0w1TMK+8PsAwDM/zGMsTJrz0xhuTjEaj4hdECLlcrsOHD69evWrGjOlz585dt27dgQP77Xa7RsPpdDqlPUWFkBwadBoTE/vuu++2b9/hX6kXGAMI+W17XB+vAHKoZCP8CwgRu1N310DTxEcAoFWmQ+61DSXTKAUEgBDtbTfwm/52fbYSWk2h2K2QZYQt/4gnzqma/q+ZJNX4OI6bMWMmQujbb1cajUb47zYRwEfzZVnWbDYTQnieP3XqxNy5Rz788MMOHdp17nxd9+7de/fubTAYqZn0hhv6Llr0+cSJEzZv3qTXG2oaPhM4aIs4r9fbrFnWDTf0SU9Pj4iIKikpvnDhwvr1648dO8ZxXJDDcGhOoSRJbrebVsDyer2TJk1KTEzq0aNnuG1Tg4NOgcPheOmliRs3brRYLIQQ2vmZ4zie56GPyYQQ4nQ64+Pjb7ihX2ZmRnJyMs/zZ8+e27Xr7507dwiCEARVr1LUlZGWhsZERFgnTnx59Oj/U+oAi6K4d+/eTZs27tr1986dO4uLi9VqNbV80ltWuqddDkqfMTExH3zwwQ039K245iEEAHh++EMuvIQsJhBqTlmEiMfLZqUbn3sIABCCRlGKECRCSB+W4en7hZ37xRPnIKcJrQhSjKGWEw+f4v/Y7kuEoDzsMy4u7v335xoM+i+/XKLV6qpKOSLlbbVZlmVZVqMBhJBdu3Zv3bp12bKlqamp11/fdfDgwa1btzEaTc2bt/jss0VTpkxZvnwZy7LU9FrrO6DbPlo7X/mS9grX63UvvPDCiBF3JyQkKOFtGONHHhmzcuXKhQs/stvtwexZQYsa9+3b12azbd++Xa/Xa7XawsLC8ePHf/7551lZzcNc2ICgdg5CyJQpk3/88Xur1QoAsNlsyckpHTt2pE5xeiQ9TBCEESPufvzxsS1btlLixQAApaWlGzZsmD37nWPHjhkMhuC7wzEmlRbaDhzU5llScqlVq9YzZszs0+cGAIDX683OvrBhw4aff/759OlTOTk5GGO9Xm8ymZQf+rchI4RsNlubNm2nT5/Rs2dPUqF5NSYAQfHIKX791pCTk6DcKEqAfty9bGZyyLIgCDnTKAWEQJaR1YQiLd41G0OxWyGEQMbE5dEO7FVW08/nhaep8QMGDGRZdseOHTzPB8gcEEK1Wq3T6QRBuHjx4p49e1atWrVjx468vDyWZZKTU2+77Taz2bJt2zZqVqrdtVP9lOd5s9ksSZJyalEUDQbju+++93//97DZbFa8ldQCaTabu3fv3qJFiy1bttjtdjZYBZwQQh6PJyur+fvvz2UYtGfPHp7nTSbThQvnT548deONN+r1+nAQaUOByuU5c2Z/8MEHJpNZlmWn09m1a/d58+abTKbvv1/tWy9QEIQnnnhy1qxZ8fEJilWD/stxXMuWLW+++eZ9+/adOXMmmO5wUK4Xer3eWr9T9PVxOByDB9++ePF/W7VqderUyfXr182f/8GMGTPWrFlz7txZnuf1ej3HcQHuPqlW7XA4brrppvnzP2rXrh19EysudQg9365zf7sO6UOvuChCxOnibu9nfvkxiBAAEITqaxqSRAgAZT5VVrqcWyj8fTAEo2Ygw8gXC9QdWqqapVaIHYUQ0hbVPXr0yMjI3L17V0FBQYBV05SMIo1Gw7IsIeDcubPr1q3dsOG3TZs2XbpUfOedd/bs2fPo0aO5ubnVFqe4HNTMYjDon3nmGYzlkydPUYc8fcemTp12zz33YCzTvHWlmzZ9dTHGTZo0iYqK/vXXXxECddZBrTrQdOZ77x05bNgdaWlp+/btzcvLt1gsR44czsvLu/nmAdRaG+bCIIPuxVet+m7q1CksqxJFgRAyZsyYd955NzMz45tvVuzatYtSC1119947cubMtyGENHLEd3XR0SwWa/v27f/888/i4uLgaFrULqLT6Z5//gWbrfTUqVPUYllTGhZF0WQyPf30M5MmTT548OD06W99+OGHX3655MiRI4QQlUrFcVpqGQpwZIZhvF4vAODRRx+bNeuduLi4CrGmAJSV2MYlNtsb83CJPeSS6KlRNC3J+t5EJjoilLeDAIAQ22n5ghAAgPGZ+1WtmhK3N7Q2hYQABgJJcH31AwAAXEYJlCQwxkOHDl227Os+fW4oLS2taNbwN3xZUSUIAcdxUVFRdrv99983TJ48qX//vuvXr7/33pFt27ap0etKT2232zt27PTpp4ueeea5goIiKpKoOtytW7f77htFL77iKweAku17xx13DBo00OFwBs0gSS1OHo8HAHDnncM/+2xxnz43lJSUmEzmVau+mz9/HijfWIQRNFC5vHfv3ilTpvA8z/PeiIiId955d+rUaZGRkV6v9+zZswhBUB7okZqa+vjj40B5xn2F1aVEQrVs2erhhx+B5ZXog3AjdDPXpct1y5eveOSRRyGEXq+nRiomISQqKmrkyFEmk/Guu4aPGnXvypXfnD17xmAwGI1GOpQsywEuUfoo7Hab2Wx5663pU6ZMMZlMhFRm/ycEAOBZ+5d44ixUhVg1LtpuFiLj8w+yzdIAxqHMgiCkiZDWXUuON738KNJzQJJCK9aIAKBSC7sP8Vv3Aggur+xHtV2McYsWLRYv/u8TTzwpy7Lb7b5cClRzHkJkWVapVBaLRaPRFBQUzJ8/b/r0t0RRCjxuhSqYkiSNHfv4l19+1b9/f5fL5Xa7KAvSDKf773/Af2t45ch+/W7U6/WSJAVBVFGBKElScXExAECSpPbt2y9evPiRRx51u10sq5o/f94vv6ypae3HMK4ElAULCwteemlCdna2KIqtW7f5z38WjRp1Hz2AEGK325WeIbIsd+3avUWLFv51wXJNa3hCQqIg8CAodjTaLP7YsaORkZGzZr0zf/6HcXEJdrv9cl3QD7Ra7erVq19//bXdu3cTQkwmk1arxRgHvgWkoNqA3W7v1q37smXLR4/+v/ISu5WxIEJEkj0//k48PGBDLI8eIeJ06+4aqL1zAACVbBVCDaF9fQgCQriBvfQPDsNub2jZlwmBGjXOL/b8+LufoygXmkymadPe+uSTT7KymtlspRgHujVUoFTQ5jjObDYLgnDkyFFJCqimNm2BFhUVPW/e/Lfemh4VFQUAKCkpEUVRSdIwmUytW7epdih6uq5dr4+PjxcEIWg6OyHEbrcBACjhmUzmWbNmvfHGGxqN+tKlS5MmTTp27Fj9BdOG4QtKZoLAT5486e+//0YIDhgwYNGiRd26dVPiPkRRvHjxosKCWq22a9frqx2ZHh8TE52VlYUxBkHc4yi5jEOGDF2x4psbb7zR6XTKsowC2MdACLOzs/Pz82juEyWzmi5FuhF0uVwIoSeffOqLL5Z06tSpcqcgBSEAAH7zLnHHQaQLsRLbCBK3V9W2mWniI1BFq4409CVVh9AmwnIYnntQ0609cXpCy0CKMdRz3nVbxMMnAYJVrUWlV9ngwbd/8cVXI0bcJYqC18vXdGsIyk2mVB/XaNTV/pq+RQ6HvUOHjl988cXw4SN8S/oqGqQsy3Fx8bR6RbUDAgASE5PK64YEaYFTMUE/U8IjBDz99LPTpr2VlJR05MjhqVMnl5SU0EcdnEu6ZkGf8H//+8W3334rSdKdd9758cefpqWlU2uB4vBT9CSMsUqlatq0KQgga54e0KRJkwaZRmrzyMrK+uST/4wb94QsY1GUAtFZVSqVUoy0FiuQrtvS0pJmzZp9+OFHkydPiYiIlGW5yl0pAQAhgIl3zUa5uBSoQqkTPYRAxkCtMj4/mkmIATIOLUteFQglXqkUtHOvyWB66VFkMRJBDKHHSghUq+VzOfwfOwGgGZCVH6iUjMnIyPjoo4Vz5sxNSUmx2ajXsMYBL+Unr2bp07fL6XTeddfdS5Z8SauxKK4+xQepjBf4c/V4POW6f8O8fsrzvO+++997b05qatp33303d+779K9hLqw/YCzTaqLvvPO2IPDPPff87NlzaMKDr1/NN2daSZwIZHwq9wM8uA4By1OBqcnBbDZPnfrmzJlva7Vaj6d6l2GtlxyEECHG7XYTgh9++JGlS5fffvsQejH+TkoIAEA8fsazZjPSa0NrOwghcboND92hva0vICFXU7QqhDwRAqr7YE3PTsbnHgKiCEDQ9iGBgAAV61rxi1xQDGDVTFjuMqT1A0eOHLlixTcjRtwty5Lb7aqRNyJA0BwJt9vz3HMvzJ07Lz4+vkLUmUajYVmGvr8Mw+Tk5DgczmqHpS98bm6O0+kMWgYFhST9K92KbnYxxgMGDFy8eHH79u3ef//9n3/+qa4yo8O4HBhjhJji4uKpU6fk5eVOmPDSpEmTtVrt5Z4/llXFxMTQiUAIiaJw4sRJEFhrZULIyZMn6ukWqoKvJVMpgf3QQw8tWvR5eno6tVjW+UmpbcPhsDVt2mzhwk9mz56Tmpqq9KWp8mekLPzS8/0GXFAcWt5BBhGnS9Ork+Fp6i0OydJglaExECEoq55geHSE9pYbiNMVQgZSAqCKlU6e967bAgCodkUqxqL09PQPP1zwzjvvpaamOByOus0Kp4FwLKt6881pr732Gi3YXWH8mJgYjtNgLINyJ+LOnTuqHZmKtj179hYUFND693V1zdWeNCoqEvxb9S7nQrlz5y6ffvpZs2bNJkwYf+bMmbCzsF7x9tszN27c+NJLr0yc+BIoj2aqcAzDMBaLlZrhEUIej3fbtq3Vjkwn99y5c0ePHq1FalCtgRCq4BdQgsj69Lnhk08+zcrKcjrrMkyaqsVut1uW8ejRD3/zzcrBg29TvB6BqAs4v8jz4x8AhVK5MoSAV2Bio02vP8FEWWntt4a+pkARMoziHxACjIGKNb3xONsklbhCJpuCEMAygBc8P/6BeT6Q1lHK1pBlmVGjRi1fvuLuu+8WRdHj8dTJ1pCWSWNZdubMt8eNG6do5ZcfptOVVUOl1/P1118LguCHRaj5i+f5NWt+drvdwam1Rnd4arXGao0Al2nK1LKEMW7dus3ixf9lGPb111+j0iS8L6xb0Ke6cuU3n376yWuvvTZhwkQ6FxWWFv2SZdmoqEhCCLWRaDSaLVu2bN++nbJLpeOT8m5/y5cvy8/PD1pOPcZYrVYlJiZV+F7J6GjXrv1nny3q1auX3W6rE3pmGEaWJYfD0aJFiwULFsyc+XZiYqJSjzuAKyYAAPfq3+WT56E2ZKrJUBENgPG5B9WdWwEcklVPq0bjuVaEAMZsRrJ56lNQqwFiyDgLMQE6jt95QPjrHwCq3xRSKFvDJk2azpv34cKFn7Ro0dLhcPjzkAcAhBDPCyzLvvPOuyNHjlTSBC8/kmXZZs2yaJ9bQgjHcdu3b1u4cEHZPf3b5098WpIuWvTZ+vXr/LcXqFtIkpSZmaH0q7ocCCFZllu2bLls2bKTJ09+9NFHIOwprFNQFjxy5Mibb0595plnX331dapqVLq0aAXtlJRUQggAZQV4Cwry5s6dI4pipXnlNAiMYZitW7csWfLFZW2G6hGEEABQQkJCpX+lV5uV1Xzhwk8GDBhEA7JqfS76atvtdp1O//TTz3z77XdDhw6jCRKBvvWEAAYRm8P7859EkkOIbBAkTrd2+M36/7sDgBBqPR8gQuY5BgKEACHczT0NY0YQrxAqnkJCIMuQUrtn1W9AqkE/YSXRkGGYYcOGffXV0qeeelqlUjudDlDZHi6QAUVRVKtVM2bMvPvue6oysyjZypmZGfSP9L8sy8yb98GXXy4B5W+sLMuiKCp1QBCCixb9Z8aM6bWuRFUL0CTClJRUo9Hg5zCqvLdq1frtt2ctX77s0KGDYQNpXYGKaUmSZs9+Z9CgWyZPnkILJ1UluCnJZWVlybJMD5Fl2Wg0rVu3dvz4F202mxKuJUmSJEmULxmG2b592/PPP5efnx/MYrYY48TERLPZDKrwzCGEMJbj4uI++GDewIGD7HZb7biQ5vJ6PN6+fft9/vniSZMmR0fH+FFVKwfNmti6V/j7ADToQqUTPULE6Va1yzI+P1rpptfQ11QzhGqJNT+AUN02SzhwXDp2DmpDpPQagSqVfCFH06tzhSaF1UIJ+jCbzb179+7cuVN29sUzZ87IslwjvqGiSqVSzZz59qhR9/l3NlDmc7lc3333HQ15p1q82+3+44/fc3Nzk5NTjEYjbZFIq30ePXps5swZ8+bNo/E+QZNTCCGHwzFs2B19+/bz70mle5S0tDS9Xr9q1Xd9+/b7VxfvMGoL+gwXL17E88LUqW8yDEMr8FV9PIYQud2eH3/8QSnsTu3bu3fv3r17d2xsbFRUJMdpEYI0ajo7O/urr76cNOn1c+fO6/X6oGkwEEJBEG688cbbb7+dXmel9wUhwhgbDIbevXsdOnT4xInjNbLc0pFtNltiYuL48S9Onjy5SZOmyp9qxoIIAUG0z/xEPHYGakKjBy+CRJSRTmud+4q6XfNGZxSlCL3uE/4BIcAYmo2Wac8Wj3pRys6DWq7hreQEABbhS3bX8p/VHVvWQiei2xcIYe/efTp16rx48eKPP15w8eJFrVbLsmy1RkgaI6pSqaZPnzly5KgAQ29SUlLj4uJo2Asoc5aoMcafffafNWt+vv766zMzm5jNZrfbfeTIkW3bthUWFuh0uiC73zDGOp2eJvtXe16qUtx99z08L3z99fIHH3woGJd4VYNuB/fv35+Tkzt+/IRAdAtaBiU1NaVFi+Y7d/5Nq6LToXQ63datf+3Z80/Hjp3at29vsVgkScrJydm5c8ehQ4e0Wm0wWRCUE2GnTp1Ylq1WzcIYx8bGzZ49+//+b/SBAwfKU2n9gQ7odrtVKtXIkSOfffb5Zs2akfIm9bW7ZmHvEX7T35BThwQLUlknisaXH9P06txIWRA0PiIEACAEZMxmpZlef7zk2elAkgATGgHELMP/vkM8ekbVPB2QGud4KPYinU73xBNP9O/fb86c937++Wen00lFSVU0QPdzoii+8cYbo0aNCqRdLT0gJSWlS5frVq78RmnqRKWeyWQqLS1dvXq1IAhK8r5WqzMajX4uoz6AEHI6na1bt+7YsSMIzFxMb+2uu+7asWNHQUF+TExseFN4JYAQut3ugoKCxx4ba7VaA5HgVB2Jiopu167D5s1/0UbTFIQQnU6PMf7rr82//baelm5hWVar1ZlMJl9XdBBAXQnJySmB1FQC5a7o9PSMuXPnPfrow6dPn9bpdH4uGCHE87wgCO3bt3vmmWcHDbqVKpqgVo4PBe6lP2OHCxr0Db8BAABAQBwe3cjB+sfuAqDxuQYVNEr2BggBArRD+hvH3ks83pB4+phATiOfvej54XcAAg2ZuRwKHTZv3mL+/I8+/HBBhw4d7XY7LYdWlUB3Op3jxj0xduw4KvQDIUIawnD99V0rvJNUGLEsazKZoqOj4+LioqOjTSZzjWrn1xWon7Jt27YJCQmBEDwoVws4juvQoYPXywf4qzD8QJKknj17RkdHB141nqJnz55RUVGiKPpOAWUCvV4fFRUVFxcXExNrtUZQhgjy6kIIuVyu9u3btW3bBgSW40jfgrZt286YMdNqtdIo60pHBgA4HA6r1frii+OXL/9myJBhyqazliyIMYBQ2H3Y+8cOwLINVcviX0CIuDzqLm1ML4+BLANw43MNKmicRAgBXQf6p+7jbr2B2BwgiFlH/sAg77q/5IJiwKBal3vwya9ghwwZ8vXXK1555dWIiAin01Ghfgcoe5mdgwffNn78BOXnAZ4FANCnT5/MzEy3210pHcqyLEmSLMuyLAc/CJMq7Gaz+eabB9T0hwAAk8kUERERjh29QgiCoNVqOY4DAS8t5ciePXtlZGT4NqlXoKwuusAaZJowxhoN17NnL42GC1xhovvCfv36T5o0hX72/SHlObfbLYrSsGHDvvpq2SuvvEprCwSaHVEVaCf6n/+ULuRCrabhq8kgRAQRRUWYXn+ciY8O/f4S/tE4iRCU9aZAOs4y7Vm2TRZxugDT0PeCMdRpxb1HyyquXZnKpuRXREREjB8/YcWKb+68cwQhxOFwKHs+WqW3TZv206dPp2WuaiSqMMbNmjXr3buP0p43pED9N82bN6fNvmuxsaMVkOv+yq4lsCxbu8oJhBCz2Tx48G1BNqcHCGq3TEtLGzz4NlDD1UX11JEjRz766Bi321VhTKfT2apVq4ULP16w4OOOHTuW1zKsSWjo5cAEQChfyPWu2QS5EGBBmjWIsWnCw5ruHRqva1BBY756BIGMmeQ48xvjUKQF8GLDTwYhgEXOz7/DdmfgeRRVQcmvwBi3atV6wYKFH3740XXXXedyOQVBYBhGEARIRGHYAAAgAElEQVSr1Tpp0qSkpORau99Hjx4dHR1dwX4VIsAYjxhxF/Ue1eLyQlD+Njr4iaX0A+X4oUOHpqSkBK1RSY0gSeKIEcOTk5NrurpgeWHSp556um/f/l6vl+oKNpstOjpmwoQJS5cuHzp0qFLRqa7u3f3zRun0BRgKJbYhIA6X4dER+oeGNcZkicvR0MxxhWAQwITr39U0/mFACMByQ08JgWq1dPCEd+POuhqRGluoe2bo0GHLln395pvT4+PjnE6n1+u95557+/btW1PnDQV9P1u3bnP77UM8norW0YYFtS917Njx1ltvrfUgISh8rx1Qk0N6esZ9991PqyY19BX9Dwghr9ebkpJ6zz0jQa0UJnp3kZGRL7zwvNlsLi4uZhjmnnvuXbZs+cSJLyckJFRV0ak2IAQgSFwez8q19OrrYMwrAYOI080N6m2a+EjZN43/RWvoZ3rlQBAQon9omH70ndjV0IEzBACWIV7es/wXIghlscV1ASrTqblp3Lhxq1f/MHbsWK1WW1JSUnbm2r7MAIAHHnggKSk5dKQVvTCE0Nix4+LjEyq4YcJoLKCzdv/9D3Tq1Llua3VeIagLfPToh+l2sHYXBsu6LRJB4Hv16vmf/3y2cOHHrVu3piacK7WF/vtyAQDub9eKh081fE01BhGXR9Ui0zzlKajXNa6Con7QCBPqKwVCms6txcMnpWOnoUbTsKYDyCA5t0jdNovNSK5Du4HyahFCrFZrv379W7RoiTHu1q2bYqupxZgY47i4OFmW//jjj2BW9PADWgR88ODBEye+RC1LYSJsjKCry2AwaLW6335bRwtwN/RFAYZh7HZb9+49p0yZUtMgoAqAEJ45c6Zdu3avvfZGy5atqDuwjpvJEAIQwg6X493PpePnoI5rSOGGIOFFZDJa5ryk7tgSYNLw29M6wlVBhDTLXqtRd2olbP5Hzi2EXINyIcsQmx0yDDegJ6Q5jnX3YlBWoHTVtGnTjh070dIwV/LuQQjbtGlz8OABmtTcsFxIC9kkJSXPnftBTExM3fblCCP4gBBmZWVlZ1/YtWtXKKwuSRLNZsvbb8/Kymp+JasLljWpTuzUqRPHcXUQF1opCAEQen/b5lq4DHINWkiL2rckyfzmM7oh/a+CABlfXC13QktypyeZ33kRRZqIp0HbU2ACdTrPmo3iweMA1D6n0A8UOtRoNFeoflK1XafTTZ48NSMjw+12N2CkJYRQliW1Wj1lytTmza9IToURClCq902c+NJ1111vt9sbdnURQrxefsKEib1796mT1UXDZOogLrRSEAIQIh7e/dWPxCs0OPEQt8fw+L36B4ZcHQEyvriKpAzt39u1venNZ4GKAZLUYFNFK8S7ve5lPwFQj57kuqp2RmNTs7Kypk17S6/X8zzfIPRD5RTPCy+88MLQoUNr7bwJI6RANa2EhMRp06YlJSU1VFgWJarS0tL777/vkUceAXUUS1X3ttB/jw4AEHYfFHYeaODtIMMQh0s7qLfphYfKvgkTYegCIUCIfvgA47hRRJQAaLjZggAwyPvrFvHE2ToMmankPHV0g5RTBw26Zfz4CQAQSZKDLK3ojTidzpEjRz355NPBPHUY9Q2qaXXpct3s2XMMBqPX6w3yvpAaLW022513jpgyZRrtPlYn704QvNeu/34vl9gA23BZEwxDHE5151bmmS9Cg/7q2w6Cq8RHWAEQqq9vh/OLhJ0HGsxZSABUqXBxKYqyaLp3bERL57rrrtPrdX/++SfGcnBa74JyGnY6nQ888OCMGW9znCZcIPSqRGZmZlxc/F9/bXa5XEGLzKILqbS0dPjwEe+9977FYm4cJneMAULC7kPOeV8CSWowuyhCxO1h0hIjPp7CZiRdZa5BBVcdEdLAGZZRd2kjHTklHT0DtQ0UZwUhkGWcW6Tp05mJsoZ+IT6FeLp0uS42Nnbr1q1OpzMIjcJp6wxRFEePHj1t2lt6va5xyKkwagJqmcQYt2nTpnXr1lu2/FVUVERjTOr1vHQz6na7hw8fMWfO+2azqdGsLmoj+XCp948d0KBrmGoyCBEvD80G69xXNV3aXK0sCK5CIgTlXKjTarp14LftkS/kQq4huJAQqFbJ2flMTKSmewd6ZaHSTLgKKOkZ7du3z8zM3Ldvb3Z2NsfVV+slCCHDMC6Xk+O0r7zy6sSJL1PJ2DjkVBg1B+XCjIzMTp06Hzx48PTp0xqNBqF6aW9JV5fH44EQPPHEU9Onz9DpdI1mdWECIBT2HXVMW0Awpp2tgg0qSBlkfvVx3R03lXVDvEpxNRIhKJtCZDaq22Z5N/6Niy5BTQO5mhGQz+dph92I9DoAQn1TCHw096ysrBtu6FtQUHDs2FFBEDQaTd2ehWEYURSdTmfr1m3mzv3g7rvvrl01rzAaF+jqSkpKuvHGm10u94ED+z0eD8dxSlJQnZwCIQZjyel0ZWRkvPXWjDFjHmtkXZrpdnD+l/zGXQ1j06I6sZc3Pf+Q4clRgJDQ1+OvBFcpEYIyLmQSYtjUBH7LHuB0AxUT7NYlEEDE4BI7Exel7tQaYNJYCrTDspZyUbfeOjgqKvrEieO5ubkMgxiGuUKFmobYybLsdDrNZtODDz707ruz27RpU8fFOMIIYdDVZTabbrrpppSUlPPnz50/fw4AyLJsnawuQojL5VKrNcOH3/n++3O7d++hnLcuLr/+QQiAULqQ65j+MXa6G6a4KITE7tQ9NMw8+cmyszeWp1crXL1ECMq4kG2ahqxmfsP2sm+CDIQAL+KiEm5QH2TQNaKoGaXOWadOnfr168dx2nPnzhYXF9NWUDSLP3AVXmnDxvO81+tVq9VDhgydPHnq6NH/ZzQawyx4rYGuLghhmzZtBwwYYLFYT506VVRUJMsyQkilUoGa5N9CiBgGQQi9Xq/X6wWA9OzZa/LkyWPHjouKimp8qwsTgKBzwXLvmk1Qr20A7yCDiN2lHTHQOvN5qFZd3UZRiquaCEFZNQR1m2ZQr/P+tgUiFOwZJQRq1PKFXDY1Qd2hRSMiQuCjQUdGRvXt2/fGG2+0WCzFxcWlpZdsNhshRKVSKSXQ/IPneYfDgTFOSEgYNGjQW2/NePjhR9LT05XaxI1JToVRF1Bm3GQy9ejR45ZbbrVYLHa73el0lutbSKma5AcAAFEUXS6nJElxcXE33NB36tQ3n3rqqVatWtMkjUa2ujAGDJLOXrRN/Yg4XZBlg23HQog4XZreXSzvjEcRlqupjpofsA19AUEBIYZHR8i5Bc6PlkKdtl4T+yoHhO4ffudu78dEWhojF1KdulmzrFdffW3s2MfXrv11+/bthw8fOnjwoNfrZVkWlhc7VcJtKMMRQjCWIYRNm2a1bdu2TZu2AwcObNq0Kf1r41PVw6hTKKsFAJCSkjJx4ktjxz6+devWjRv/2L9//7Fjxy9dKq6gadEfknLIMmYYmJqa1qJFiy5druvf/8ZWrVrToxrt6oIAAH7dFvn4WRhhBrIc1JMjiJ1uddss6wevlrfbvfpZEFwTREh7SCJkenkMLi5xffkjspiCSoQYQy0nbN/H/7FdN3xg8M5bd6BWTVpTODIycuTIUffeOzI7+8LJk6fOnTt79uyZ06fP2GylOTk5LpcLIcgwqqSkRJPJlJqalp6enpaWlp6enp6eUW7yIvVVmDGMRghF2QKAmM3mQYMGDRw4MD8//+zZs2fPnjl69Oi5c2ftdvv58+ddLhddM7GxMZGRUQkJiZmZmU2aNElJSUlLS9fr9aBM92q0q4sQgCAuvORa+hMIfqMJhIiHZzNTLLPGM0lx1w4LgmuCCEFZxRmoUZsnP03cvGf1b9BsABgHz+aAEJBl13+/195yQ1n9+ManqJbRoUJjyckpyckp9Buvl5dlied5xWuo0WhYltFoOEUe0d43FVT7MMKgqLC64uLi4uLiunbtSgjhebq6BNqQixCiVqtVKpVarWbZ/0mwq2Z1ub9dJx49HexAdwYRN89EW63vTVR3anVNsSC4VogQlDkLUaTZ8t5EIAieXzZBowGQYClchECOE/Yc9ny/QXfvrQATwDTWd5UKGsU2Rf+r1XIAAIPBcPnxhOBy3m+cSnoYQcTl1nWEEG2WpNdXcjwhhBBMf9foVxchAEI5t9C94tcyHgrajhBB4hWQSW+e8Zyme4drxC/oi2vpbsuTC83Tn1d1bo2druBNNiGARcAruL7+BTucgEEN2zHxykEFlhKGQKoGhAghBGGjClgIo6FBVxet5OB3dUGEmEYWDlMVCAEAeL7fIB48DnXaILIgIoKEDFrL7InawX2BjBtLllcd4loiQlDWoYJJjrPOeUXTqTVxewATrCcgY2jUCdv2eH/bDkC99GZqQPgP6gsjjFqj2pDRqwSYAIRwUYl7+RqAUPBS1xECkgQJNr3+hPa2vkCWgycSQwnX3j0jBDBRtciwznuVzUgmTneQJ971+bfY7qRuy2CeN4wwwghxuL/fIB46CbWaIOUOIggkmciyacpT+vtvvxbyBavCNXnbCAIZs03TrHNeZpLiiCtY+0JCoEYj7Dni/WNHME4XRhhhNArQYNFLNvfSn4KnlyMIRBnIsnnSk4ZH7wKYFlG7ijbZNcE1SYQAAAYBGau7trPOe41NiicubzDWHwGAQcDDe75bTxqwb3AYYYQRUiAEAOD9+U8paMGiEAIZA0xMb4wzPHY3ZeKruJRotbhWiRCUcaGmR0frgklMQgxxe4NhFiAEaDX8pl38+m0AgGDnCYURRhihBkIAQviSzf3desKLgEH1ntYFISCE8ILp9bGGsfeUFdS+tnENEyEo3xde14ZyIfB46p0LCYEqltidruU/E1EMewrDCCMMAIDnh9/57fugQQfkelaOKQs6XYZx95azYJgHr3EiBOX7wq7trHNfRQmxxF3/XChjaNDxG7Z7f90MwNUWPhpGGGHUAIQACHFxqWvRt8EQBQgCQojLox9zt+mlMWXembCPJkyEAJRzYa9OEQsms0lxxFP/NlKEiCA4P1lZdq7Gw4VVpXPhf8NP4lf9nbTOzxtGgzx5P0MFeNL6eBT1BUIAAO5vfhWPnYWcpn5FAYRAxtjpNoy9yzz5SahR0/a/9XjGxoNrprKMf1Ab6fVtrQsnX3pssnwxr34TWgmGWk7cc8i96jf9yMGhU3HNR4iUfVS+Ke+aCyo1o9RrRldVgwd+UkU+4vI59f2tUjH8iq6ysYGUV0Wv8A3wKe9y5U++FvAzeIDnLb81XN5HL1TnmnoH84vcK34p69pdf0QIIZAxkWTjcw+ZJj4MVaqyAJkwAABhIvwfKBd2aWOZ+3LpM9PliwVQz9WXvZ4AwCDscntWrtXe0rusCHiwXk5F3l0uBH0qdFQuK2RZ9nq99IPT6aRHSpLodrvB/1z8MDc31+PxVCh5BSEsLS3JycmpUSksjElUVFRsbCytM+nzPdbptLGxMQgxikzX6w20ETkAgOM4jUZDD9ZqtfSktC9PVY+F0mQFsldOGirSMzBUynDKR6Ugmf+b8nq9kiRBCGVZdrncGMsIQVnGLpeLlJcnJIQUFRU7nU7foQghDMM4nc6LFy/KshT4o8MYm83mhISECvoWHTA+Pk6tViu3o9Vq1WoN1XK0Wq1GoyGEqFQqOu8QVj7XhBBZlkFlBFntA6ljEAIg9KzeIB48AfW6etS8EQKSRGRsnjSuzC8YMsp3iCBMhD5gEJBlrldn6/w3Sp58s373hTJGBr2wdY/3t2264QPqe136qv8KD13+zsuy7HK5ioqKBEG4dKnY4/GWlpa6XE6v15ufn08IcDrtBQUFECJRFC5cuIAxYRjG7Xbl5+fTksd0HEmSqrgMjDGuoWu+rEhbpX+j/eroZ4xxfHyCwaCXZUwIiY6OtlgssixDCJKSkjUaDSEgJSUZIcSybGxsLIQwKipKrdZERFh1Or1Wq/VDk8BnW3mFu4rLt2IUV1gqpcKwvk/s8mExxk6nk+f5oqIiQnB+foEkSaWlpU6nAyGUl5fndrsJAbm5OV6vFyHE8968vHxRFBkGiaKUk5MjSaJSXU+uolWQolvU6D4CnG4AQHR0jNFowJgQgqOjYywWsyzLRqMxJiaOEGyxWCIiIliWjYmJRQjFxcUihKKjY4xGI+2CUtU1K7Ncv6RICEBIzilwrfilfjkJISBLQPJhQRD2C1ZEmAj/DYYBGGu6t7cueKPkianyhTyo4+qrygP1Wy9erR3UC+rrpXm98lb7qv+EEJ4XvF6P2+2+cOFCTs7FnJzcnJyLhYUFDofTbrfbbDZRFGw2G8/zTqfD5XLRco50HMoWEEKFNnw/U1TNKEwthEtVXp/L5eyFC+eVg48ePaL8VZIk+r0sy4QQlmUjIyMBAFarVaVSm0wmjuMMBoNer4uOjomMjIqLi42Ojk5MTIyOjtZotBqNWqPRVChoWb59DJQUK0jYSn9C/V41qh9d6RTT70VR8Hp5URSLi4vPnj3rcNizs7NLSkoLCvIdDkdpaakoCqWlpYSQ4uJiWZYdDrvb7abVO+k4Pp0mAcOUyYrLp9tPqc86nG7gY9mmuHgxWzn4yJEjivWbEjMhRJIklUodGRmBEIyIiGIYZLFYjUajyWSKiYmJiopKSkpOTk6Oj48zmy0ajYbjOF+uDQIpen7eKB6oz8qiCAFJArJsmvyk4bG7ASYAhlmwEoSJ8DIgBDDWXN8ucvHMkvGzhF0HkUFfL8uUAMhphH8OedZs1o0YQCVgHYxamcD1eDznz5/Lzc0rKMg/dOjQ+fPnjh8/XlRUTHsniaIoCIIoigghlmUYhqXCDiGo1eoMBiPdbCjjV/hQ6TXU4k81xeWE4Sug/Ri+CCGiKAJAcnJy6IaGSk+MsUqlYllWVQa1SsVGRESmpaUlJiZmZmbGxcXHx8dHRUXGxsbpdDrljiqwUYX7JeU9OujZ6T6spKQE47KNFMZYq9XFx8cZjSZlBCr0K2XE8jGBMskAAEEQiouLCwsL8vLy8vLyTp48mZube+bMmfz8fEHgeZ6XZVkQBEmS6L2zrApCSNsYsSwLIdDp9EajCfg1nleK4Ew3uEzBCmS6qUJACDh//hwAQJJOYCxLklw+xSqNRsOyrMFgSEtLy8xs0rJly8TExNjYuKSkpIiICB9SLPM41k2Pi7Jg0RLXlz+UVRatD2UbISCIBALzpDALVoMwEVYGhICMVa2bRsx5peS56fzOA8hkKDOs1yFoZT9Zdi36luvfFUWYr3BTqMhc5V09derkP//8c/jw4TNnTh89evTs2bM8z1PxQbV4WuCfZVUajcZXEfb9tyo7Zyig1iKYPiK1mqmwpaPPEGMsCALP8xjjoqKiY8eOKt9HRkYmJSWlpKQ0a5aVnp7esWOnrKwsZUtUoSu6L5mVlJRs3751z569p0+fys7Ozs8vkGWJnpoa9FJTU2Nj41q0aNG7d++WLVspzZB9B6SX4bsDO3v2zIEDB44ePXrs2LHc3NwLFy5cuHAe/ht0liGEanXZ1rbCI2oUcw1qO90IMQAApXOhogxRHcjtdhNCSkpKzp8/v3HjRjplycnJTZs2TU9Pz8pq3qFDx3bt2lFdAdRV43tMAAPdy9ZIx87UVykZhiFeHhn1ljef1t01CGAM4LVbQa1ahImwCjAIYMw2T4/4eGrpxHc967cgo55aM+vyLBhDnVbYdcD9za9lhY6uwPMEIYAQCYJw+vTpTZs2rl+/7tSpU4WFhTZbKUIMx3FqtUqJH1F+BWrly6lWEIRIaEm1orPSAyhzgErugng8nsOHD+/du1eWZWphS0hI6Nq1W9++/Vu3bmmxWH3HRAgRgk+ePLF06dLff//t/PkLRUVFlJB8O8pCCAsLC48fP87zPMdxCQkJrVq1GjHi7j59boiMjFD0G2XT6fF4Tp8+uWnT5t9///306VOXLpVculQMAKBbHJPJVNVDUKT/5bfsX7g3+GwGsrP0Y0Kv6q8Mwyimft9fFBQUXLhwQRRFnU4XHR2VnJzaq1fvfv36tmzZij7eK7LfYAIYJJ3Odi35AUBYL30HESJuDzTqLe+/rB3U+1rrslsLhImwaiAEMGFS4q0fTYLj3/Z8ux6ajfUS4oyQe8Uv2tv7MfHRteNCjDFCSBDEP/7Y8P33q9etW2ez2TDGDMOo1erIyChlQ0Nq0ou4wm4JQggAwfh/YoXSp2IYpC4u5ZJ85W+lRIsxuUKTEISowtOq4K9SrGfK94rE96U65csKQvPyy2YYhmVZaheVZTknJ+f8+fM7d+5cuHBhhw7tb7996JAhQ2NjY+iMnD596ssvl3z99dc5OTksy6rV6oiICD+3YzQaCSH5+fkXL178/fffO3XqPGrUfXfccYdWq5VlmWEYQRDWrVu7YsXXW7b8Zbc76BSrVCqrtYyAK33UygOp4Cr2vVnFr3a5buQ7lQAAWcYVZk35VW0BIayEaytMpe8MKptaxXWtDFTpVPrnyMsPUKlUarUaISTLclFRcW5u3q5df8+fP+/6668bOnTYoEGDYmJia8+FEAAA3N+tE4+fQVYzqCLUqPZgGOL2oMRYy5SntIN6X5v9BWuKMBH6BYIAE2Q1Wd4eD9Vq98p1UKuhX9bZKTCGWk48cNy9ar1x3MhaDYARQvv373/vvff++GODzWbT6/Ucx1HpELi9y5cS6GdJkmRZlmWZetAkSWIYxLIsQohhWIQQjb5jGIZhVBjLZrM5Li6WDhIXF2cwGGnis0ajSU1N8xUahBCNRhMdHU3NVrUAhFAUxcLCAlEUfeVRQUFBaWkpvQWv13vx4kWMMUKwsLCwuLiYYVhJEmkmhiiKdHtEvYPURcowjO+/LMv6kGIZbfsyDW2eTmljx44dW7du/e67lW++Oa1Ll+uWL1/2/vtzDh8+rNfrjUajQkKyLEuSSB+tci8sy1JKQwhptVo64M6dO/7+e+e6db+++OL4Nm3a5ubmvvXWm999963Xy3Mcp9VqFYnvO5Sv+kL/q0yiJMl0Riit0lgYlUoFIaBTCSGi+SdaLRcfn6BSsbKMo6IiIyMjaf66Wq1OSkpSclQAAISAyMgIg8FUIx3r31MJbDZ7aWnJv/18KD8/r6SkhHZ19nrdubm5kiQhhGw2W15ePkL0SUqgPBhKlmVZljAmoijSrR6dROWzQorVMreiUkAINRoNTczAWN68efOmTRu//nr5U089M3DgwNrcLSYAQeHgcfeSH1B9xMgwDLE72aaplvde0nRrDzC+NvsL1hRhIqwOCAJCUKTF8sFrKCbS+dFSqNEAlqnLFQwBYBjX59/pbu/HJMXVaFNIWfCXX9a88MJzubl5er3eYrFQSV2ttdNXd6avvSzLoihiLAuCCCE0mUxms1mv1xsMBo7jTCazxWKOjo4xGAxxcXE6nS4xMREhxmq1xsTEYIxZlqWmVyoxWZZRstaCaVtT0gExxjwv0C9pNBDDMAUFBZcuXQIAZGdn87zXZistKCh0Ol15eTleL2+322k8rcPhcLmcpaWlABCGYSltUCXg8kcHAEAI0W3itm3bnnrqybZt2/3880+CwNMtIJXOXq9XEASDQR8REWk0Go1GAx1JFCW7vdRmsxUUFIKy9Dg1QshgMGCMv//++4MHD06c+PI336xYu/YXk8lsMpmUuiq+igv9ks4jJQn6j8EHOp1Op9Op1Zro6OioqCiDwZCQkMiyTHJyMsOwWq02MTGRbrmo25gQQi2uJDhJBZVNJf0gCAJ9MyRJ5HmRYZDNZsvNzQEA5uRcdLvdJSUlhYWFTqcjLy/P5XK53R6Xy+lyuex2u91uEwRRpVLReVSpVBX2jn62jD6GbsZgMBBCdu7c+eijDz/xxJMvvPAizWuswTOh28HFq6TsPGTS13FEOkLE7lB1bm2ZNV7dNgvIYRYMFGEiDAAQAkIgy5jfeAJFmh2zPgOCANSqOlvEmEBOLZ+56Fryg+nlMdSRHtDvMEYIbd686emnny4tLTGbLVQGVvtDKs1pGKGS/8cwTHR0dEZGRkxMbGpqamRkZHR0dExMTEREZGRkpMFgMBqNNb0zRT5gjCvICkLqIKSwglgmBPhmoTEM0unKVrgS5OnHOCnL2G632Wy20tLSoqKiS5cuFRcXFhVdKiwsKCjIu3AhOzc31263070FIRghhkaZ0l0XvR2LxXLu3LkTJ07odDqj0SRJEs/zkiTp9fq+fft17NipadPMxMQkykP0FnheyM/Pz8vLPXXq1NmzZ7Zt23748CEIgUqlVqvVFoslJ+fic889gzGmPkg6xdRUSKNAfTLEiVqtSUxMSkiIT0hIpGeJiYmxWiOioqIiIyPNZrPRaFSr1bV41OUP+V8KVoUKRLXD5fRKyP88tQAAmv1Z4VcRERHp6emVDuj1el0uV3FxcUnJpfz8gsLC/Pz8wry8nLy8vAsXLmRnZ3u9XgghXfyU6UF1uqOy8zYYDKIozpr1ttFoePLJp2tAhBgDhIR/DnvXbYGaGk+BP1BGd7o0vbtYP3iVSYoL7wVrhDARBgbqGoTA+OR9yGS0T1uAXR7IaerSvq9i3N+u095xkyorPRDnNiEEIcTz/Gef/Sc/Py8qKkoUxWpPQqWn2+3med5kMickJCYkxLdt265ly1bNmjU1m80REREmk1mr1V7+Wz/Op0DOe9lvA/xpDQAhCNxddflxCEGr1Ur9bb6QJNFud5SWljoc9ry8vHPnztIozZyc3NLSUputhBCg0+mUOE+VSsVxnCRJdrsdQpCWlj548G233HJrZmZmVTQcGxvbtm1bUOZ3vLhv377ly5dt2bKltLREo+GUzZnvFLhcLkEQzGZLTEyM1WrNymreoUP7+PiE5ORkumu0WCwcxwFALq9gUPVUVnLwZUdWKBjk//Bagk5ltYf5hhH5guM4juNotqgCSRLtdntJSWlJyaXTp8+cOXP6n392nzx5sqSktLS0BABAVb1qTSmyXJZ9sf7n0nIAACAASURBVHjx4oEDBzVp0pSqpNVeK32p3Uu+ly4WIJOhzqxKEAIIic2hu3uQ+a1nUYQlHB1TU4SJMGBQLiRE/+BQJjay9MVZcn4xNNZR2xRMoFYrnbngWvK9ZdqzAKFqJRIVAdnZF44dO67TaQPZCEIIadZgq1atunXr0bFjhx49eiYkJCAEK5xMyewGl4VahDgC5+bLj6wQPwLKtQ2WVUVERFAOa9eu7GCMcX5+3u7du3fv3r1nzz+HDh3yeDzU2kYp0GqNaNOmycCBAx54YHR8fDx9npUWO/U9I0IoOTklOTll0KBbNm3atGTJF/v27cvJuehyufR6va9zrn37Dh07dujUqXO7du3S0tJ9E2B8hy3fiwc4lSEUVRHIVFZYogoqRMHQh8+yqoiIyIiISAAyO3fuQv9eWFi4a9euPXv2bN265e+/d2JMqI/W/3mpulNaWpqfn9+kSdOAlC9CAITCjv3uVb8hfd15BxEEEiaCoH/8HvNrj0MtpzBuGIEjTIQ1QVkmEeEG9rKajaUvzRYPHEdmY92saYyRVuv5br12yI2aLq2r1c3py280GqOjI0+cOP7vtIjKjxdFMTOzydixY/v165+YmOhr0lE4APpEVF5rUJ5GpSylCDvKdgih+PiEW2+NHzz4NqfTOWHCiytXrtRqtaIoxsTETJgwMTOzSVJSUmJiou8gVT1Y34lQsg/79u3bvXv306dP5+XlnTt37quvvjxwYL9Go/F6vc2bt/jkk08zMjIqnUTgwxD+68Zdrah0Kiudx+jomIEDBw0adEtJScn27dsWLFiwY8d2Gh3j/xSkLGc3sMdLCECI8KLj46+J21NnlUUZRDw81KhML48xPn1/WSbGNfnyXiHCRFhDQAggADLWdGsf8fl02/h3+M27oV5bB+n2hAAVi/OLXJ+u0HRpHfDloEBeRbpN0ev1r7zyyq23DlbKUPmGfgQzCKJxodJtBynPGIEQarVajAm1j/E836FDh+HDR9AwCiX/OkDdwjeCA2OsVqtbtGhBc/aLigr/+Wc3x3GEEI7TmM1mAACN/wThSQwAl8+jb66I2WweNOiWFi1ajh794IEDB/R6fbU2Up980+rOTQiA0PvrZn7Dtjprt8QwxOlCkVbTK2P0991edpYwC9YK4adWKzAIYKzKSI74bJpuxEBid9aNIkYI1Gm9v23z/rYNQBiI0VWn05nNlgqdGaoYm6jV6ujoaPqZxpSHRWetodAbzTbzenn6PSFEp9MrUZ1+6nBWO77idKQSOSoqmgpuhkHnz593uVyg3OkbnsTaQZlEqkwQQqKjo41Gk28F+ap+KEmSj0e5GjcGQAiXOlxfrCIuD2CZOiBChIjDyTZJjvjPNP39Q0C4lPaVIUyEtQVCAGNkMVneGW8a/zAAgPDClXIh3RTaHK5FK4mXB4y/nr00RkCv18fExCitAPxeL/J4PHl5+SAECoVcZZBlOT8/l2aFS5KUkJBI8wuv3MJMxTSdr6SkpHJ7HRRFMcTLoTVGQAhzc3OLigp980ergizLVqvVarWAal8oQgAA7u838Jt2Q8MVRxVACCDEdof6+naRi9/W9OgQLp925QgT4RUAIYAx5DTGiY+Y33oO6Tji4a+UC2WMjHp+49/en/6s9li6UUhISIAQVZsgzLKs3W7PyckBdV0KOQyXy1VYWEhnAUKYnp4GAgg+DBxUzsbGxtDYYFpi5sSJE3U1fhgKCgryi4uLWFbl/x2BEIqiEBUVFR0dA/wTISEAITm/2LVwOWCv2F+LEMGYuL26oTdFfDqNbZYGZAwgCrPgFSJMhFcGWjkeAP39t1s/mcqmJxCnC6ArW5cQEgk7Pl6Oi0v9V3Sjr19mZmZUVCRtoOr3ShHP88ePHwXXaixM/eHw4UMOh0OlUkmSGBkZmZiYVB9niYqKTk1NFUWBYViv17tnzx4Q3tzXHSjzZWdn5+Xlq9XVECEhhGHYtLR0UK3GQwgAwPXlD+LJ85C7svraDCK8AGXZ+OwD1nmvMfFRtGxpKIX6NlaEBeIVoyyUFHP9ukZ++Q7X93pidwJ8BX0kMIZajbj3uOurnwAA1RJh+/YdYmPjeJ73LxMxxlqt9vjx43l5eZWmXoVRC9DHuGPHjkuXitVqtcfjTUtLa9asGahTbYOm8MfHx7do0cLj8bIsIwjCwYP762r8MED5Q967d2+1R1IDuMVi6dChYzWHYgwQEo+cci/9CarY2menQAgQIk43spgsc14xvfIY1GpotbZaDhjGvxEmwrpAeQl5NjPFumCy/qFhQJSILNe+sgOEAEH3sp/E42dp7W8/x6akpGRkZFQbL4Mx5jhuz549p06dBGHraB0BISRJ0r59e3ieRwgJgtC0abP4+Phqoy1qCrrtaNeuvcGgpwndJ0+eOnPmTFinqRNQm3ZBQcH27ds5jvO/yaOZSEaj8frrr6tmXAgBwe7/rpbPXYScupYpExACCIjDrWrXPOKzabq7BpaNE2bBukOYCOsONHwm0mKe9aJp8jgIIHF7Qe2yuDCGOk46dsb56QoAaL3TSo5ShODNN9+sVmuqzalHCLnd7q1bt4Jy/bc21xZGOai43L9/3/79B/R6gyAIRqOpe/fu9XEuSqvdunWLiYn1eDx6vf7UqZObN28CdeqMvMZx8OCBY8eO+lYtqAoY444dO8XElJWYr+ogACH/1x73t+sgx9WyIiNCABNsc3K39o78fHpZHe0rdL6EcRnCRFinQAgQAiEyPHZPxKdT2dQEYnPUMqCLEKjXelZtEP4+QP9bxVEEANCrV2+TyRjILkSlUv34449FRUU1vp4wqsBff/117tw5juMEQYiKiuzf/8b6OAud2ebNW7Ro0VKSJAiRJEnr16/zer0Mw4R1miuBUqdt9epVSudq/8cDAIYOHaZSqap86QgACBFBcHywRLY5gIqtjXcQIcLzAGPTsw9a571WVkE07OCvB4SfaV2DZtxjzA3sFbHkbe7mHsTprs3yJQSwLLE77LM/xx4vbYJR2dkgACAlJbVv375ut9s/EdJOAqdPn/rjj99B2Dp6ZaA5gqWlpStXrqBJ7qIo9u3bNz4+HtRDDAuV1AzDDBlyO8Mwsizp9fpNmzYdPHgQhKfyykCf3uHDhzdv3lwtC0IIBUHIzMzs0KE98DPRBAMAPN+s5f/ajWqXQY8gcbqY6EjLuxNMr49DRj3A4Xz5+kL4sdYDyl2Gqmbp1gWT9ONGAkKIl6+xmZRgqOWEzbu9P20EoPJNIYRQlmWWZW+5ZTAtZeJvPEJUKpXL5VqxYgVtvxcWoLUGfXTr1q09duw4ffIajfr224cwDFPnDkJf9OrVJzMzQxAElmUdDseSJf8NqOJzGFWDTuWaNT/Tnb1/UzP1LwwefFtqalqVE40JQEi+kOv8ZAUgBDA1zKBHCEBAnG51j44Ri97S3XNL2c/DTsF6Q/j9qTcgBDBBZqNl8pOWOS8z8dHEUcPMCmpdkSTnR0ulC3nU7lrZeRAAoE+fPh07dnI6nf5VWoyxwWD466/Na9f+AsLupdqCbgcdDucXX/xXlmW1Wm232/v169+1azdQbykNtGZ3UlLSHXcMd7vdtGXgmjVr/vlnNwhPZW2BMWYY5ty5c6tWraLNmPwAIeT1epOTkwcPvs3fcRAAAFxLfxIOHIc6rmYxMggRXiC8qH9oWOTn09WdWgEZAxiuGlO/CBNhfaJcg9MNHxC55G11t/a4psXYMIY6TjxwzL30x6oOofLRYrEMH36HSqXyHz5KzWs8z3/22Wc2my3sXqod6ENbuvTLHTt26PV6QRBUKtWQIUN1Ol29bgcpbr11cHp6hsvl0mq1RUWFixYtAuHc0NqCTtbnny86evSwVqutTp8ggiDcdNPNHTp0oMpQJYdgAiAU9h5xLV6NdDWMkaE5EiaDZfqz5hnPI6u5rK1gmAXrGeGXp55R7jJUtWoa8Z83DWPuAhAQr7cGXEgA0Ghci1eJB44DCCt9r+jLfOedI1q1akk7jvoZT5Zlg8Hw119/ffvtShB2L9Uc1BR5/vz5L774L5WGbre7Z8+ed955Z5XCsY6AEMIYt2zZ8rbbbhNFEWOs0+l/+unHtWvXgvKGvWEEDhom8/fffy9btpTjqum+BCEURSkqKmrUqFHAz4uDIADAtWCZXFBcgxgZWlfW7lB3ahm5ZJZ+9J0QMeEi2kFD+CnXP8pdhkxMpGX6c5bZL7GJccTpKvu+WhAC1SpccMnxwRIg40qjZmgkhcViffzxJwMRxLSC5cKFC44fP05la+3u7FrGJ58sPHTooMFgkCRJo1E/+ugYhqm+QGVd4aGHRmdmZrrdbrVa7Xa7Z89+J7y/rykoC4qiOG/eBwUFBdVWk6Eaz7Bhd9BehpWrmzIGAHh+/tPz619Ip60066kSMAzx8kSWDY/eFfHFLHWX1mXJ8uGNYLAQJsJggebFE6IbPiBi6bvczT2JwwVEKSAHOMbQoPP+stnz0x8A+Ks1M3jw4L59+9rt9mo9hbTKzDvvzJJlKRw1EzhkWUYIrVu3dsmSJQaDEUJot9sGD76tX7/+ICgFz6ji0qRJkzFjxtDGFHq9fvfu3Z9++kl9n/oqA13zS5Z8sW7dWoPBIPuthU29g6mpaQ8++CB1RlQy14QABsn5RY65S4iXD6jLBG0ub3cwCTHW2RMtM55nYiIAxuG4mCAjTIRBBFKiSdMiPp5ienkM1KiI2xtQBA1CRBSd87/ChSWVRs3Q8FGtVvv0089GRkYKglBtoRmj0fjjj99/8803IBxqERiohzU7O3vmzBlut0ulUvE8n5CQOGbMWI1GEwTvIAU9y/Dhd3Xr1t3hcCCEOI6bP3/++vXrqIwOwjU0dlD79oED+99/f04ABm1ICMFYfuyxx1q2bFVlmC4BAADX4tXCroNQF0APegYRWSZOF9e/W8QXM3V330KvLGwODT6YyZMnN/Q1XGOAEBACNWpNt/bqNs3EI6fl8zlQraoqKLQMhECVSj6XA7WcpmensnH+DbqxS0lJKS0t2bx5M01u83MhtJHe3r37evXqHRsbGzQ53kihWNLeeOP1devWGo1mQrDH43n++RfuuOPOYOYwULbT6XTx8XHr1q0VBEGtVjsc9mPHjt100wCzufp2etc4yj27rmeffWbfvn3V9uBlGMbhsPfo0WPatLdUKjWodOuPMUBI2H3YNvVDIEnQ/2KAADCIuDyI0xiefsAy/Xk2MTZsDm1AhImwIUB7SmDCZiZzt/QBkiweOE48XqhRAz9eBQgBQtKRU5rr2zKJsWX+wkqOgq1bt9m2beu5c2f9+/9pq97CwsIjRw7feONNRmNAtWmuWVAi/OSTj+fPn6fX6yGEDoejX79+06fPZFkWBLcRBOXCjIyM0tLSjRs3aTQatVqdnX2hoKBgwICBtJ1eeCqrAq2aPWXKlG+++YYuez8H0xKyERERs2fPSU/PqFzjIYSmPdhffV/85xDUaf0ptQgCTLDDpencxvLueP2o2yDLAhKuoN2QCBNhA4HWXSMYGXTcDdez6YnCwRM4rwiq/G0NoYrFJTZ8yaYd1BuqVYBU7HFB5aNer8/IyFi7di2tv+XnKgghGo3m1KlTly5duummG1m26pJR1zYwlhFifvvtt5dfnkhrXnu93tjY2Pfffz8pKbmhUtohhG3btv3nn92nT5/UaDiWZQ8c2M8wTI8ePakCFJ7Ky0FX+OLFn7/33myNRuP/YNrC3uv1vPLKq8OG3VG1UZQACN1fr3EuXAa1nL8RGUQ8PERIP/oOy6wJqpZNyvr0hlmwQREmwoYDLN8aQqhqkcn1vR5fsouHTgAZQ7W68p8QAjVq6dgZJi5K3aEFIJUYSGkEaUpKKoRww4bf1Gp1IAVI9+/fjxDTo0dPZZArvr2rB7IsMwxz8ODBJ598oqioiOM4WZYJwZMnTx4wYFADsiA1kDb9//bOPD6qKsvj97736r3aq1JZKxCyFGQjBCQhCUsIJGyttgzSsf3Yy+i0tu2ooAM46qdFQLDZWj7Noo7T3dNO2/px6RmYdmMJJJIdCEkIJEUwIUslkI3Uvr5354+bPEpIKnSLZrvfP/JBU/Xy6t5653fPueecO2NGQUGBxWJhWZaiqPLysilTpsyalUqcwtvBU/n555+99NK/4+rPwNsHNE2bzf0PPrh28+ZX8Zpy6BwZiuJNXf3/tkvotw6sUIe4FgUQQlY7kxin3blB+eSPKaWclAmOEYgQjjbYNRQQFayVrcqm9WHeukahqxdKmKE7M1EQ8IK3volbkkGHBuEO97ddEgIAMjIyTCZTRUWFTDZygRRFUaWlpRqNOi0t3f8iBNx5pK7uwrp1zxqNRryfZLPZ/uVfHt+wYRMepNEaK7zomTJlamho6NGjRwVBkEgkPp/vq6+KoqNjkpKScHcFMpUYrIJnzlQ+99z63t7eEcvnaZq2Wq333DP30KG3NBrNsEFRCAEAli0HXEWVA9p2C/g0QYcTAih/+D7tb1/g0lMgfiJJXszYgAjh2ABCvDZk5yRyi9MEm8PXeBW5PJCT3LpriAAuKxRsdtkPFg/3IOGHNj193oULtY2NjSNqIe6QWVhYqFQqcZkUIFo4aDqNRuPTTz9VW1urVCoBAA6HIzc3b9eu3XK5bNS9LqyFM2emOByO4uJihmEkEonD4aioqEhNTY2JiSGdSDF4QVNZWfnkk0+0t7crFIrA/QcoinI6nREREfv3H0hMTMTfhCFehxCA0Hm4wLr3j5CRDOHbURQQBGRzSFLiNdueVT79E1qnGdjgn/TP19iBCOGYYcA1FOiwYNkPsulpkb6GrwVT95AJpZChvZe+lsRGSZINwzmFuK1oSsqs4uLT3d3dgVtyI4QYhuF5obDwlFqtTk8nfuGA6ezs7Fy//tmKigqVSgUhtNlsKSkpb731ll4fOeoqKAIhTE9PM5lM58+f5ziOYRizub+0tHT27DnTpk3DtY+jfY+jCZaxs2fPPPnkL1tbW1Uq1YgqyPM8x3Hbt+9YuXIV/iYM8TrcXNt0rf/f9/KdPVDGfqPxE4SAppHdARhG8ega7d4X2PQUCCFAgIRDxxpECMcYuIkaBSUzZ0iXLUBOp6+uEbm9kGUGWvliaAr4BG/dZdnyBVSQBrc3vO1KUBCE8PDw5OSZJ04ct1qtd6KFgiCcOnVTC8eOrf+ewaazvv7S+vXrS0tLsAo6HI64uLj9+w8GKib73sFOIcty8+fPr6ura2iol0qlEomkp6enuPj09OkzDAbDZM6BwjJ25kzl448/3t7eNqIKQkgJguByubZs2fLznz8aaKIRABBa9/6X67MiqJR9QwVpCnh5ZHdKZsVrX39e9eSPB85RgpDkxYxBiBCOPbDBQogK0khz5zPxsb6rJqG1E9LUzV1DBCDL8J3dyOaQrVyE9+GH08Lo6OjExMSiokKz2XyHWlhYWCiVSmfPnhPo6NGJC1bB6urqdeueqagoV6vVAACXy6VWq9555/eZmZnDBspGCTFbOCsr69y5s1evXmVZlmXZvr6+Y8eO6vWRKSkpgiBMtmUNQgiXDJ4+/dVTTz1lMrUrlcqRVBDyPO/xuF9++eVnnlmHR2yYs5YEQFGuo8WW19+GNH3z6YMQUJRgtVFqpeKxtdpdG9h7kgFCAAFAQTCJhn88QYRwTDKYQQMpKEkySPOyKFbivXAF2WyQZQcjpQiyEt/FRnpqhCRlxpBCCAYf7OnTp0dGRp48WeB2uwNnymEtREg4efKk3W5fuHDhpNJCbDppmi4uLl637hmj0ahSqSgKOp0uhULxu98dzMvLG2sqiMFaGBQUtHDhorNnz7S2tuIYqdPpLCw8KZfL09PnDdsbbCKCPTkI4fvv/+W559Z3d3ePqIK4fZ3b7X7xxZc3bNgYqAQFZ4p29Zpf2MO3dkLx6F2KQj4fsDuli+dpX39O8diDlEI20Dt7cgz7OIUI4Rhm8MmhNCouJ4PNTOVN131NbYAXICsZOK3Q5fY1NEmXLaB0miE3C4HfkQXJyTOLigrvJEZKUTRFUZWVlUZjQ0ZGplqtFgQewjERCfzuwGNCUdTHH3+8fv26zs5OuVyO9wXVatWBAwfvv//+YbeLxgBY53Q63cKFi6qqzjU3N0ulUppm3G73yZMnnE5nWlr699kKbhTBKujxePbv/922bVudTqdcLr8zFXS99NLLGzZsxElqw6jgQHabZdcfXEdOQpVioC8aQoLVzuhD1S89odn8r0x8rKiO38mHJNw9iBCObQbq7hFAiInSy+7PoUJ0PmOT0H0DMhSgKMix/PUe1G+R3bdk4PVDXwbiTs1JSUmFhSf7+80cx43YgA3XF5aUlCYmJkZFTcPe0oS0oWIMzWq17tmze+vWzS6XS6FQIIScTqderz9w4OCqVaNWMnjn4ABAcHDwokXZNTXVTU1NLCuRSCQURRUXn25oqE9LSwsK0k3gMCn+aBRFtbe3bdy46Q9/+D1N0yzLjtg+xuv1ulyuF198eePGTQgJgcpOBAFQ0Hm4wLLjbcixAwfK212QgvK1K7S7N8lWZd/sdzERB3niQYRwPAAhABAgAbISNj2Fy8kAbo+34SpyOiHDQE7ivXCZDgtm5yQOmTUzeA3I87zBYEhISKyoKO/u7h6xGSlCSCaTtbW1FhQcl0pls2fPoWl64qUgYhNJUZTR2LBp07+99957LMvhhYLVaouNjfvjH/+4cOGisRkRvR2c8ajT6ZYuzW1ubr506RLDMDRNSySS+vr6r74qmjp1yowZ8RMyTIo/EUVRRUVFzz23/tSpAtwMb8TaIZfLxbLsq69uXb9+faB9QQCAgABN8e3X+zftFnpuQJkUuNyC08VlpGq3PqP81cO0PmwgNkPCoeMHIoTjBHjT26NDddIViyT3JPLt1/jWTvxkei9e4RbNpcOChwuQgsHgj8EwPStrfl3dBRw6C2wmcA82m8124sTxlparSUnJwcHBE6Z9lxgLFQTh448/Xrdu3fnzVQqFkmFoQRCsVktOTs6hQ4dmzUoVN5xG+5bvCNx+Xa1WL1mSa7NZKisrKYpiGIZl2Y6OjqNHjzqdzpSUVLl8oKJ8vHyuAIg+vd1uO3To0Cuv/Lql5apKpRrxfDGaph0OR1CQdufOXY8++ugI323cEZTn+3+9z110BsqkqN/KRIar1v1M8+rTkjlJkKEBAEQCxx1ECMcVg7WGgIJMXJR89TI6PNjb0IR6+1GvWejslq5cBDl2uMQZMBgj1ev1eXnL2traLlyopWkm8IGuCCGJRELTdFVV1YkTxzUaTXJyEkXR41oOxTAvhLCp6evXXtu6d+8em82qUCgBAD6fz+Vy5uf/+I039sXExGJfcHx90sE8Unlubp5MJisuLvZ6vQzDcByHW89UVpZHRUVFR8eIJzeNrw8oIk4lRVFnzlRu3Ljh3Xf/hIMZgcOh+C1mszk5OengwTdXrfrByI14EAIQ2t49bDv0F8ALkGPlD9+rff052Q9zoUw6EI8Zn8M4ySFCOA4ZrK+AEoadmyxdmgVYCd/c7jlzYeCQpuGFEAzGSDUazbJly30+X3V11YhNF/GvZDJZX1/fl19+0dTUPH369NDQUPFX48uGigE0t9v9l7+89+KLL5w6dQrX3kEInU4nwzDPP7/x1Ve3aLXa8RIRvR2scBRFLViwIC4urrKyvK+vj+M4iqI4jmtubjpx4nhvb29SUqJKpUYI4WEZR1OJ7xkAQFFUd3f3/v37N29+pb7+klKpwl5+gPfiF1it1nvvvffAgUOpqbNHdvp5AdCUt7q+f/0OZHVwizPU255VPpFPR4QOtFUjZfLjFiKE4xNx4YkQFaLllmRIF6cjm93x12NM3FRJYlyAACkYDJ2xLJubmxsVFVVdXd3V1YVN5IiuIYSwurr6+PFjDoczNjYW19gN3tRYtwKired5vrS09NVXX3nzzTf7+814JwkA0N/fHxcXt2vXnl/84hf4cKVxvSE6OCMoOXnm/PkLmpqajEYjzp3hOKnD4SwtLS0pKZbL5TExsVKp9LY3jlH8vUC323XkyJFNmzYcPvy/Xq9XJpOP6AjicChF0c8+u27Xrj1hYWEjqyBCgKKEPvONX20BCKg3P6158ZeSZMPAW0gsdJxDhHA8I5YbQkDrQ2Wr8+joKe6CMklCDBWsHdEvxP+YOTMlOzu7ra318uXLAIARXUPsT1gsllOnTpaUFEul0tjYWHyczZj1DrHdBIOqVlNTvXPnb3bs2H7p0iWZTIYP6HC5XD6f79577ztw4MCCBQsnTLtq/CkEQYiMjFy16gcICTU1NU6nE4e7WZY1mTo+/fRvtbW1CoUiNjYWu79jfCqxBHo8nqNHj27btuXQoYNdXV0KhQK3gwjwdtxT12q1JiUl7dy564knfonLZEdY7iAAIEROl/33n9BTw4P2bOIWzoWsBABAYqETAyKE459B1xBAIEkysHNnCte6qTAdpGmAQIBOFqKJDA8PX7XqXqVSUVVVZbFYRjykDRfdcxxnMplOnDheXHyaZbmIiHDsV+H8dTAGzCi+DdFuCoJQW1uze/fu3/zm9bKyMoZhZDIZzhWyWCwhIaGbNr3wyiuvRETox1dqzJ2A50Umk+XkLElMTLx8ubGtrU3MJmVZtqGh4ejRo5WVFSqVSq/X+69swNibSqfTef78udde27Zv3z6jsUEul+MCiQBrOOwI2u12QRDWrv3Rvn37MjOzBAFBeAdOPwQAIb6lgzFEy9csg0o54IUBCRztkSHcFYgQTgjEZ1IQKJWCjgjBmQB30s8Jm0iplJs/f/7cuWkdHR1GYwNFQZpmAwaKEA6uAgBaWlqOHTtaVlZut9uioqbhIxpGVxH9d7wghF6vt6ys7O2339y+fXtJSYnX65XL5dj8uVwur9e7fPnyPXt+u2bNGny2+wRTQYz4iRISEpYvX+71euvq6ux2Ow5340MWGxsb1DfJ1wAADRJJREFUP/vss0uXLlEUnDJFL5XKwKhOpah/eLJwbsvx48ffeGPvzp07a2trGYaRy+UjSiBeBlkslhkzpm/evHXTpheCg4P/rhwoxPOUVk2HBd+skZ9w35DJDBHCiQWuvqco+PfsbIkVFNHR0ffdd69WG3T5cmNvbxeuqR8xUopDiy0tVwsKCgoLC69cadTpgnU6Hbaw4Ht0LNAgFEXh/c6enp4vv/x8x44dBw8eKCoqAgDIZDKGYbA62u2OqKiol156+de/3hwTEyM6HN/pTY4i4vhrNJrc3LzU1NT29vbm5mae5/HGIcuyEIKLFy9++eUXJSUl1693hYWFaTQaUTAGp/J7mEkgpvXiPV2Tqf299/78+uvb//Sn/6qpqZFIJLj4Z8SkGACA3W7jOO6nP/353r1vZGdni7J65zcE8TNFauQnKEQIJxz/0FMqhkk5jsvKylqxYrnH47lypdFsHjj3PMB7sXHkOI7jOJOp/ezZM0eOHC4rK7PZ7BzHabVaLDzYduLF+11xL0R3QbwaviBFUV1d18vLy//7v9/dsmXzhx9+aDQaIYT4BAkAAM/zFotZpVL/9Kc/eeONfbm5ebiAZEI6grcgTjSEcPr06T/84QM6XVBTU1NHRwdN01jwpFIpRVHNzc2lpSX/8z9/ra+v93g8NE3rdMF4iCC8ueYQL/tt7gr5Id4khFAQ+NbWloKCk2+//daOHds//fTT9vZ2lmXlcjnwW2AN80kpioIOh53nfbm5edu2bX/qqac0Gs0I9fKBmehfj0kLM9o3QBhD4AgSQshgmL5799777//hO++8c+rUKZ73SaVShmECdGvEC3OFQomQ4HK5iooKT5w4PmNGfGpq6pw5c5YsWZqQkHCLporp79gqibbpdiMlmjxR/AAAOK3D31O5du1aRUV5VdX5c+fOVFVVOZ1O3HharVZjI+vz+dxuN8tya9Y8+M///GhOzhLsDU+MvJg7B7vLgiCoVKpnnlmXl7fsgw/e/+STT65fv4a76uB1Aw4nfvTRhx999GF8fHxmZta8eRkZGRlxcXG3BBX9e7b5///hptL/J/bC/V/p8/mamr6uqqoqKSm5cKG2ru6CICCO46RSKe5tFLhrKM6IcbtdPp8vJWXWr3715H333a9Wa8Rai28/gIQJBhFCwjfAZgIhRNN0bm5eVtb806e/+s//fKe8vNxsNiuVSmxDh1uMCwIPAGAYBm/etLW1Go0NR44cfued/wgPD58z55758+fPnJkSEhKi0Whw4uLtF7nt4nBIjRQEweFwmM3mlpar1dXVZWWl9fX1PT29N2700TQtl8vF0g6EEM/zNptNo9GkpaU///zzixcvpmlGTEH89uM27sDaj0cgKSl569bX8vMfeuutN48dO9bV1aVQyFlWCiHExzkhhL7++uuLF+s++eSj4OCQ6OiY+fMXpKfPMxjigoKClEqlRCIZ8q/cMpXiguOWMRcE3mKx9vT0XLnSWF1dXV5e1tzc3NvbazabpVKpXK4QbzWABIqfyGazAQCSkpIeeeQnP/pRfmhoqBgwvwsDR5iIECEkDAE2KDjPcOXKVXl5eZ9//vn7779fWHjK7XaL22zDbc+I632O43CDj66uro6Ojtramj//+V2JhI2PjzcYDNOmRev1kSEhIaGhoVqtJiJCzzAMToK/5YIOh93n4y0WS3d3940bN7q7u7q6uq5evdra2trQUN/V1QUAwAUPHMcFBQWJCicIAm6mrFAo1q5dm5//UHb2YtxNe/xWyt9FxHg1AGDWrNQ333y7oqL8gw8+OHmyoK2tleOkeLGCEJLJZPgAh2vXrplMpvLyMghBUJAuNjY2NjYuJiYmLCwsPDw8NDRMpwvS6XQUReNlk/+fw6ULCAk9Pb03btzo6enu6elpbW3r7DQ1N7cYjZesVhtCgiAgXNoREhIiCELgXBgAAHYovV6v2+1GCM2Zc8+aNQ/m5+eHh4fjiZ4MQW/Ct4EIIWFoxPW1IAg0zTzwwOqcnCXl5WV//esnBQUFFotZEASFQikG2Ya8CLZiAABcrI31SRCEurq66upqn8+LENBqtRqNVqFQ6HRBNE3LZDKFQiHaPQiBIAhms5nneez/Wa22/v4bDoedYSQMw0gkEpyVI/qyuPgBh/VomgoODsnPf2j16tWLFmXjVtp4h4yooAgeOjwsmZlZGRmZtbW1f/vb/x0+/L/t7W12u0cmk4vHUvpPpcViOXv2bEVFucfjkUjYoKAgjUarVCpUKjVFQZVKxXE3G7tDCN1ut8ViAQCYzWabzWo2m/v7zT6fF4cQsPcpeo04lB3gtsWtRIfD4fV61WpVWtqChx9+eOnSPL1eL246kokmjAgRQkIg/JNc1Gr1ypWrli1bfvHixSNHDhcVFdbW1rrdbrlczjCMqENDLt5FpcQKhHeYsNDyPN/b29Pd3XXlSiNCSBB4nhduWb7TNIOljmFoiqI5TiqXy/33mTD4r+N0UJVKmZmZmZ29OD//IYPBIAr2xM4L/Tb4D9Hs2bNnz579+ONPfPHFF5999rfq6prr16/JZDJcXyh+KxiGYRgJhAMhBLfb3dnZIe7h8TwvCN+YSggpLEv0IGq1Sgx74tcECLyDb34hfT6f0+mCEMXHJ8ydO/ehh36clbVAKmUBgLfkTxEIgSFCSLgjRCtJ0/SsWbNSU1OffvqZgoITxcWni4uL29rasMljWQ5vFw3nJt6S9oKvPBi5GrBad5JhgU2tqGper8fj8SAEGIaJjY3Nzs7Ozs7JycnBaTL49eOucfb3j7/MQAgjIiIee+yxRx555MyZyvLy8uPHj9fUVPM8jwY7Kogahueapmkc1h4u7+l2tQtc/HDLjUEIeZ53u92CwAMANRrN8uXLly7Ny8lZHBsbhxvA4O8hmWjC3wURQsKd4p9egRDS6XT5+Q898MDqjg7TuXPnvvzyiwsXLly/fr2vr4+iKLF12S1+2+2L/dsduwA34P8TIeTxeJxOJ4RQp9NFRU1LS0tftWrl7NlzIiMjGUYi+jdgMMWUcIf4+/csyy5alL1oUfbPfvbzmpqa4uKvSkpKTSZTT0+32+2WSqU4mfP2xcqdzOntDCmieKPX6/Wq1Wq9Xj9jRkJeXu6CBQvi4gy4gcPgrjBJCiX8IxAhJPx9+G/h4LrD2Ni42Ni4tWt/1NvbV1Jy+vz585cvG41GI67UZhiGpimaZrDbN1iINrSjIP4J//8SBB77DzzvEwSB5wWEBK/Xy7KswWCIj09ITEyYM2fuvHnz8IEY4r2RKOi3xH+fGAAQHh6+YsWKFStW2O32hoaGsrLSS5cuNjY21tc39Pb24toYmqb9J/qW8Q840Te3kPFPHFn1+XwQwsjISIPBMGNG/Ny5czMzM2Nj48SVjVgUQbxAwj8MEULCPwje7RPzMyGEISHBq1f/0+rV/+RyuVpaWjo6TA0N9fX1DUZjQ2trq9vt9ng8Ho/H5/N5PB4AgJgdyjA0hAMW0z9FAv+DZTmaplmWkUqlMpls6tSohISE1NTU+Pj4KVOmRkVF4U5vGLEwkUjg3cI/XgoAoChKoVCkpaWlpaUhhK5du9bS0tLc3FRTU9PU1GQ0NthsNq/X6/P5vF6vx+PB6SpwsNeBv2vu8/nEAKkgCDhlBvc+lUgkU6ZMiY6OTkmZlZw8c+rUqdOmTdNqteJ7xS8emWjCt4cIIeFb4e8ggkHzJJVKExISEhISli7N9Xg8Tqejv9/S1tba2tpiMnX09HS3tbV6PF6r1YrfZbVa3G43vo5EIlGr1QgBhqGVShXHsVFR0aGhoVOmRMbExEydGqVQKHCTZXwDaLC2bEgXhHAXEcdWDI9DCPV6vV6vz8rKys9/yOl0ORz2vr4+o9F47Vpne7upra3V6XTY7XaPx0tRlMNht9vtoiiq1Rp8XoRSqWBZNjw8PCwsIjJSbzAYoqKmBQUFcRyHm8hgxLJ9kdEZCMKEgwgh4e7gv4EnGkrcvpJlWY1GGx09DYBF/m+xWCw48NXT04PtI0JIJpOGhYXjdAz/ww5vAYfQsDUk+3/fM/4iJIoTwzAqlVKlUoaHhyclJfm/3mazud1umqbNZvONGzfwRDMMHR4egZvZqtXq4SYR+fVPJ6scwncEEULC3cffUPptC908FAr/VtS5oKCgAFcbMucCQorI31jA300U/+GX8AIAgEqlEqe0aLXa6Ojo4S413FeFLHQI3zVECAnfLX7xqxE6iN7+Fn8vkzDGGa5kAtxW6zncWwJ8VQiE7xQihIRRI4DpJEwk/OeXzDVhDEJi7gQCgUCY1BAhJBAIBMKkhgghgUAgECY1RAgJBAKBMKkhQkggEAiESQ0RQgKBQCBMaogQEggEAmFSQ4SQQCAQCJMaIoQEAoFAmNQQISQQCATCpIYIIYFAIBAmNUQICQQCgTCpIUJIIBAIhEkNEUICgUAgTGqIEBIIBAJhUkOEkEAgEAiTmv8HhHRL64O19ScAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data analysis with Jupyter Notebook, Pandas and Plotly\n",
    "## A workshop by PyLadiesBcn for PyDay 2018\n",
    "![600_336991142.jpeg](attachment:600_336991142.jpeg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. More about Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"tarjetas.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Extract people who spend money in a casino and which casino they were"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casinos = df[df.actividad=='CASINO'][['nombre', 'actividad_completa']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>nombre</th>\n",
       "      <th>actividad_completa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3354</th>\n",
       "      <td>Antonio Cámara Eguinoa</td>\n",
       "      <td>CAESAR'S HOTEL AND CASINO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3633</th>\n",
       "      <td>Antonio Cámara Eguinoa</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4884</th>\n",
       "      <td>Antonio Cámara Eguinoa</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5081</th>\n",
       "      <td>Antonio Cámara Eguinoa</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5082</th>\n",
       "      <td>Antonio Cámara Eguinoa</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      nombre         actividad_completa\n",
       "3354  Antonio Cámara Eguinoa  CAESAR'S HOTEL AND CASINO\n",
       "3633  Antonio Cámara Eguinoa                    CASINOS\n",
       "4884  Antonio Cámara Eguinoa                    CASINOS\n",
       "5081  Antonio Cámara Eguinoa                    CASINOS\n",
       "5082  Antonio Cámara Eguinoa                    CASINOS"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casinos.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's remove the duplications"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_casinos = df_casinos.drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>nombre</th>\n",
       "      <th>actividad_completa</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3354</th>\n",
       "      <td>Antonio Cámara Eguinoa</td>\n",
       "      <td>CAESAR'S HOTEL AND CASINO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3633</th>\n",
       "      <td>Antonio Cámara Eguinoa</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12448</th>\n",
       "      <td>Rafael Eduardo Torres Posada</td>\n",
       "      <td>APUESTAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19593</th>\n",
       "      <td>Gonzalo Martín Pascual</td>\n",
       "      <td>GAME GROUP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23886</th>\n",
       "      <td>Javier de Miguel Sánchez</td>\n",
       "      <td>GAME GROUP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35068</th>\n",
       "      <td>José Ricardo Martínez Castro</td>\n",
       "      <td>GAME GROUP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42105</th>\n",
       "      <td>Miguel Ángel Araujo Serrano</td>\n",
       "      <td>CASINO DE MADRID</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43936</th>\n",
       "      <td>Miguel Ángel Araujo Serrano</td>\n",
       "      <td>GAME GROUP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45385</th>\n",
       "      <td>Pablo Abejas Juárez</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51579</th>\n",
       "      <td>Ricardo Romero de Tejada y Picatoste</td>\n",
       "      <td>APUESTAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60042</th>\n",
       "      <td>Maria Mercedes de la Merced Monge</td>\n",
       "      <td>APUESTAS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60081</th>\n",
       "      <td>Maria Mercedes de la Merced Monge</td>\n",
       "      <td>ONCE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60787</th>\n",
       "      <td>Maria Mercedes de la Merced Monge</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61054</th>\n",
       "      <td>Ildefonso José Sánchez Barcoj</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66450</th>\n",
       "      <td>Matías Amat Roca</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74961</th>\n",
       "      <td>Luis Gabarda Durán</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76353</th>\n",
       "      <td>Rodrigo de Rato Figaredo</td>\n",
       "      <td>CASINOS</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                     nombre         actividad_completa\n",
       "3354                 Antonio Cámara Eguinoa  CAESAR'S HOTEL AND CASINO\n",
       "3633                 Antonio Cámara Eguinoa                    CASINOS\n",
       "12448          Rafael Eduardo Torres Posada                   APUESTAS\n",
       "19593                Gonzalo Martín Pascual                 GAME GROUP\n",
       "23886              Javier de Miguel Sánchez                 GAME GROUP\n",
       "35068          José Ricardo Martínez Castro                 GAME GROUP\n",
       "42105           Miguel Ángel Araujo Serrano           CASINO DE MADRID\n",
       "43936           Miguel Ángel Araujo Serrano                 GAME GROUP\n",
       "45385                   Pablo Abejas Juárez                    CASINOS\n",
       "51579  Ricardo Romero de Tejada y Picatoste                   APUESTAS\n",
       "60042     Maria Mercedes de la Merced Monge                   APUESTAS\n",
       "60081     Maria Mercedes de la Merced Monge                       ONCE\n",
       "60787     Maria Mercedes de la Merced Monge                    CASINOS\n",
       "61054         Ildefonso José Sánchez Barcoj                    CASINOS\n",
       "66450                      Matías Amat Roca                    CASINOS\n",
       "74961                    Luis Gabarda Durán                    CASINOS\n",
       "76353              Rodrigo de Rato Figaredo                    CASINOS"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_casinos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop NA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Pandas have special attributes called isnull() and notnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>nombre</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>minuto</th>\n",
       "      <th>importe</th>\n",
       "      <th>comercio</th>\n",
       "      <th>actividad_completa</th>\n",
       "      <th>actividad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>30070</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>22.959999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INSTRUMENTOS MUSICALES</td>\n",
       "      <td>COMPRA BIENES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30071</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>19.980000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INSTRUMENTOS MUSICALES</td>\n",
       "      <td>COMPRA BIENES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30072</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>15</td>\n",
       "      <td>53</td>\n",
       "      <td>19.980000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INSTRUMENTOS MUSICALES</td>\n",
       "      <td>COMPRA BIENES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30073</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2011-12-28</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>7.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INSTRUMENTOS MUSICALES</td>\n",
       "      <td>COMPRA BIENES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30074</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2011-12-29</td>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "      <td>102.480003</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HELADERIA</td>\n",
       "      <td>RESTAURANTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30075</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2012-01-05</td>\n",
       "      <td>15</td>\n",
       "      <td>36</td>\n",
       "      <td>0.790000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>DIRECT MARKETING CONTINUITY/SUBCRIPCION</td>\n",
       "      <td>DIRECT MARKETING</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30076</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>88.470001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MISCELLANEOUS APPAREL &amp;amp; ACCESSORY STORES</td>\n",
       "      <td>COMPRA BIENES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30077</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>88.470001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MISCELLANEOUS APPAREL &amp;amp; ACCESSORY STORES</td>\n",
       "      <td>COMPRA BIENES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30078</th>\n",
       "      <td>José Manuel Fernández Norniella</td>\n",
       "      <td>2012-01-13</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>88.470001</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MISCELLANEOUS APPAREL &amp;amp; ACCESSORY STORES</td>\n",
       "      <td>COMPRA BIENES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46845</th>\n",
       "      <td>Pablo Abejas Juárez</td>\n",
       "      <td>2011-12-29</td>\n",
       "      <td>15</td>\n",
       "      <td>37</td>\n",
       "      <td>0.990000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>INSTRUMENTOS MUSICALES</td>\n",
       "      <td>COMPRA BIENES</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64022</th>\n",
       "      <td>Ricardo Morado Iglesias</td>\n",
       "      <td>2010-12-01</td>\n",
       "      <td>12</td>\n",
       "      <td>52</td>\n",
       "      <td>2500.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AGENCIAS BANCARIAS(ANTICIPO VENTANILLA)</td>\n",
       "      <td>BANCO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                nombre       fecha  hora  minuto      importe  \\\n",
       "30070  José Manuel Fernández Norniella  2011-12-27    15      53    22.959999   \n",
       "30071  José Manuel Fernández Norniella  2011-12-27    15      53    19.980000   \n",
       "30072  José Manuel Fernández Norniella  2011-12-27    15      53    19.980000   \n",
       "30073  José Manuel Fernández Norniella  2011-12-28    16       1     7.990000   \n",
       "30074  José Manuel Fernández Norniella  2011-12-29    15      37   102.480003   \n",
       "30075  José Manuel Fernández Norniella  2012-01-05    15      36     0.790000   \n",
       "30076  José Manuel Fernández Norniella  2012-01-13    16       1    88.470001   \n",
       "30077  José Manuel Fernández Norniella  2012-01-13    16       1    88.470001   \n",
       "30078  José Manuel Fernández Norniella  2012-01-13    16       1    88.470001   \n",
       "46845              Pablo Abejas Juárez  2011-12-29    15      37     0.990000   \n",
       "64022          Ricardo Morado Iglesias  2010-12-01    12      52  2500.000000   \n",
       "\n",
       "      comercio                            actividad_completa         actividad  \n",
       "30070      NaN                        INSTRUMENTOS MUSICALES     COMPRA BIENES  \n",
       "30071      NaN                        INSTRUMENTOS MUSICALES     COMPRA BIENES  \n",
       "30072      NaN                        INSTRUMENTOS MUSICALES     COMPRA BIENES  \n",
       "30073      NaN                        INSTRUMENTOS MUSICALES     COMPRA BIENES  \n",
       "30074      NaN                                     HELADERIA       RESTAURANTE  \n",
       "30075      NaN       DIRECT MARKETING CONTINUITY/SUBCRIPCION  DIRECT MARKETING  \n",
       "30076      NaN  MISCELLANEOUS APPAREL &amp; ACCESSORY STORES     COMPRA BIENES  \n",
       "30077      NaN  MISCELLANEOUS APPAREL &amp; ACCESSORY STORES     COMPRA BIENES  \n",
       "30078      NaN  MISCELLANEOUS APPAREL &amp; ACCESSORY STORES     COMPRA BIENES  \n",
       "46845      NaN                        INSTRUMENTOS MUSICALES     COMPRA BIENES  \n",
       "64022      NaN       AGENCIAS BANCARIAS(ANTICIPO VENTANILLA)             BANCO  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.comercio.isnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "But you can also get rid of it quickly with dropna"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77202"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_drop_na_comercio = df.dropna(subset=['comercio'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "77191"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df_drop_na_comercio)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Unique values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Antonio Cámara Eguinoa', 'Rafael Eduardo Torres Posada',\n",
       "       'Gonzalo Martín Pascual', 'Javier de Miguel Sánchez',\n",
       "       'José Ricardo Martínez Castro', 'Miguel Ángel Araujo Serrano',\n",
       "       'Pablo Abejas Juárez', 'Ricardo Romero de Tejada y Picatoste',\n",
       "       'Maria Mercedes de la Merced Monge',\n",
       "       'Ildefonso José Sánchez Barcoj', 'Matías Amat Roca',\n",
       "       'Luis Gabarda Durán', 'Rodrigo de Rato Figaredo'], dtype=object)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.actividad=='CASINO'].nombre.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### String operations"
   ]
  },
  {
   "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>nombre</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>minuto</th>\n",
       "      <th>importe</th>\n",
       "      <th>comercio</th>\n",
       "      <th>actividad_completa</th>\n",
       "      <th>actividad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>38.700001</td>\n",
       "      <td>RCG OFICINA</td>\n",
       "      <td>CONFECCION TEXTIL EN GENERAL</td>\n",
       "      <td>ROPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>MANZANIL AREA</td>\n",
       "      <td>HOTELES,MOTELES,BALNEARIOS,CAMPINGS REST</td>\n",
       "      <td>HOTEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-05</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>95.620003</td>\n",
       "      <td>REST REAL C GOLF SOTOGRAN</td>\n",
       "      <td>RESTAURANTES RESTO</td>\n",
       "      <td>RESTAURANTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>49.130001</td>\n",
       "      <td>ESTACONES DE SERVICIO ML</td>\n",
       "      <td>GASOLINERAS</td>\n",
       "      <td>COCHE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>13.940000</td>\n",
       "      <td>NISA (H 24) VINHOLAN  S.A.</td>\n",
       "      <td>GASOLINERAS</td>\n",
       "      <td>COCHE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nombre       fecha  hora  minuto    importe  \\\n",
       "0  Alberto Recarte García Andrade  2003-01-04    12      30  38.700001   \n",
       "1  Alberto Recarte García Andrade  2003-01-04    12      32  14.600000   \n",
       "2  Alberto Recarte García Andrade  2003-01-05    19       7  95.620003   \n",
       "3  Alberto Recarte García Andrade  2003-01-08    15      31  49.130001   \n",
       "4  Alberto Recarte García Andrade  2003-01-08    16       5  13.940000   \n",
       "\n",
       "                         comercio                        actividad_completa  \\\n",
       "0  RCG OFICINA                                 CONFECCION TEXTIL EN GENERAL   \n",
       "1  MANZANIL AREA                   HOTELES,MOTELES,BALNEARIOS,CAMPINGS REST   \n",
       "2  REST REAL C GOLF SOTOGRAN                             RESTAURANTES RESTO   \n",
       "3  ESTACONES DE SERVICIO ML                                     GASOLINERAS   \n",
       "4  NISA (H 24) VINHOLAN  S.A.                                   GASOLINERAS   \n",
       "\n",
       "     actividad  \n",
       "0         ROPA  \n",
       "1        HOTEL  \n",
       "2  RESTAURANTE  \n",
       "3        COCHE  \n",
       "4        COCHE  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['actividad_completa'] = df.actividad_completa.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>nombre</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>minuto</th>\n",
       "      <th>importe</th>\n",
       "      <th>comercio</th>\n",
       "      <th>actividad_completa</th>\n",
       "      <th>actividad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>38.700001</td>\n",
       "      <td>RCG OFICINA</td>\n",
       "      <td>confeccion textil en general</td>\n",
       "      <td>ROPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>MANZANIL AREA</td>\n",
       "      <td>hoteles,moteles,balnearios,campings rest</td>\n",
       "      <td>HOTEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-05</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>95.620003</td>\n",
       "      <td>REST REAL C GOLF SOTOGRAN</td>\n",
       "      <td>restaurantes resto</td>\n",
       "      <td>RESTAURANTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>49.130001</td>\n",
       "      <td>ESTACONES DE SERVICIO ML</td>\n",
       "      <td>gasolineras</td>\n",
       "      <td>COCHE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>13.940000</td>\n",
       "      <td>NISA (H 24) VINHOLAN  S.A.</td>\n",
       "      <td>gasolineras</td>\n",
       "      <td>COCHE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nombre       fecha  hora  minuto    importe  \\\n",
       "0  Alberto Recarte García Andrade  2003-01-04    12      30  38.700001   \n",
       "1  Alberto Recarte García Andrade  2003-01-04    12      32  14.600000   \n",
       "2  Alberto Recarte García Andrade  2003-01-05    19       7  95.620003   \n",
       "3  Alberto Recarte García Andrade  2003-01-08    15      31  49.130001   \n",
       "4  Alberto Recarte García Andrade  2003-01-08    16       5  13.940000   \n",
       "\n",
       "                         comercio                        actividad_completa  \\\n",
       "0  RCG OFICINA                                 confeccion textil en general   \n",
       "1  MANZANIL AREA                   hoteles,moteles,balnearios,campings rest   \n",
       "2  REST REAL C GOLF SOTOGRAN                             restaurantes resto   \n",
       "3  ESTACONES DE SERVICIO ML                                     gasolineras   \n",
       "4  NISA (H 24) VINHOLAN  S.A.                                   gasolineras   \n",
       "\n",
       "     actividad  \n",
       "0         ROPA  \n",
       "1        HOTEL  \n",
       "2  RESTAURANTE  \n",
       "3        COCHE  \n",
       "4        COCHE  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numerical operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "136.06657465516707"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.actividad=='CASINO'].importe.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f5ef3c6d438>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[df.actividad=='CASINO'].importe.hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Groupby"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many activities each person spent money with?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nombre\n",
       "Alberto Recarte García Andrade            1357\n",
       "Alejandro Couceiro Ojeda                   549\n",
       "Angel Rizaldos González                    120\n",
       "Antonio Cámara Eguinoa                    3358\n",
       "Antonio Rey de Viñas Sánchez-Majestad     1822\n",
       "Antonio Romero Lázaro                     2103\n",
       "Arturo Luis Fernández Álvarez              134\n",
       "Beltrán Gutiérrez Moliner                  425\n",
       "Carlos María Martínez Martínez            1197\n",
       "Carlos Vela García                         803\n",
       "Carmen Contreras Gómez                    1061\n",
       "Cándido Cerón Escudero                     578\n",
       "Domingo Navalmoral Sánchez                 137\n",
       "Enrique de la Torre Martínez               385\n",
       "Estanislao Rodríguez-Ponga Salamanca      1414\n",
       "Fernando Serrano Antón                     637\n",
       "Francisco Baquero Noriega                 1100\n",
       "Francisco Javier López Madrid              111\n",
       "Francisco José Moure Bourio                894\n",
       "Francisco José Pérez Fernández            1195\n",
       "Gabriel María Moreno Flores                318\n",
       "Gerardo Díaz Ferrán                         64\n",
       "Gonzalo Martín Pascual                    1494\n",
       "Guillermo Ricardo Marcos Guerrero          806\n",
       "Ignacio Varela Díaz                        220\n",
       "Ignacio de Navasques Cobián               1494\n",
       "Ignacio del Río García de Sola             148\n",
       "Ildefonso José Sánchez Barcoj             2737\n",
       "Javier de Miguel Sánchez                  1682\n",
       "Jesús Pedroche Nieto                      1528\n",
       "                                          ... \n",
       "Maria Mercedes de la Merced Monge         1618\n",
       "Mariano Pérez Claver                       970\n",
       "María Carmen Cafranga Cavestany            604\n",
       "María Elena Gil García                     277\n",
       "María Enedina Alvárez Gayol                456\n",
       "Matías Amat Roca                          1036\n",
       "Mercedes Rojo Izquierdo                   1643\n",
       "Miguel Blesa de la Parra                  1565\n",
       "Miguel Corsini Freese                      228\n",
       "Miguel Muñiz de las Cuevas                 245\n",
       "Miguel Ángel Abejón Resa                  1078\n",
       "Miguel Ángel Araujo Serrano               2853\n",
       "Pablo Abejas Juárez                       1744\n",
       "Pedro Bedia Pérez                          621\n",
       "Pedro Bugidos Garay                       1159\n",
       "Rafael Darío Fernández Yruegas Moro        645\n",
       "Rafael Eduardo Torres Posada               535\n",
       "Rafael Pradillo Moreno de la Santa         120\n",
       "Rafael Spottorno Díaz Caro                1330\n",
       "Ramón Espinar Gallego                     2709\n",
       "Ramón Ferraz Ricarte                       825\n",
       "Ramón Martínez Vilches                     465\n",
       "Ricardo Morado Iglesias                    265\n",
       "Ricardo Romero de Tejada y Picatoste      2037\n",
       "Rodolfo Benito Valenciano                 1540\n",
       "Rodrigo de Rato Figaredo                   519\n",
       "Rubén Cruz Orive                          3306\n",
       "Santiago Javier Sánchez Carlos             585\n",
       "Virgilio Zapatero Gómez                    472\n",
       "Ángel Eugenio Gómez del Pulgar Perales     987\n",
       "Name: actividad, Length: 83, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('nombre').actividad.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nombre                                  actividad         \n",
       "Alberto Recarte García Andrade          AVION                  60\n",
       "                                        BANCO                   1\n",
       "                                        BUSINESS                1\n",
       "                                        COCHE                 367\n",
       "                                        COMPRA BIENES         114\n",
       "                                        DEPORTE                20\n",
       "                                        DIRECT MARKETING       15\n",
       "                                        DUTYFREE                1\n",
       "                                        HOGAR                   1\n",
       "                                        HOTEL                 189\n",
       "                                        LIBRERIA               65\n",
       "                                        MISC                   27\n",
       "                                        REGALOS                 1\n",
       "                                        RESTAURANTE           436\n",
       "                                        ROPA                   15\n",
       "                                        SALIDAS                 6\n",
       "                                        SOCIAL                  1\n",
       "                                        SUPERMERCADO            7\n",
       "                                        TELECOM                10\n",
       "                                        TREN                   14\n",
       "                                        VIAJE                   6\n",
       "Alejandro Couceiro Ojeda                AVION                   5\n",
       "                                        COCHE                  77\n",
       "                                        COMPRA BIENES          67\n",
       "                                        COMPRA BIENES LUJO      4\n",
       "                                        DEPORTE                 4\n",
       "                                        ESTETICA                4\n",
       "                                        HOGAR                  44\n",
       "                                        HOTEL                  36\n",
       "                                        IMAGEN                  9\n",
       "                                                             ... \n",
       "Virgilio Zapatero Gómez                 SALUD                   8\n",
       "                                        SOCIAL                  1\n",
       "                                        SUPERMERCADO           14\n",
       "                                        TREN                    6\n",
       "                                        VIAJE                   7\n",
       "Ángel Eugenio Gómez del Pulgar Perales  AVION                   6\n",
       "                                        BUSINESS                3\n",
       "                                        COCHE                 360\n",
       "                                        COMPRA BIENES          57\n",
       "                                        COMPRA BIENES LUJO      1\n",
       "                                        DEPORTE                10\n",
       "                                        DIRECT MARKETING        2\n",
       "                                        DUTYFREE                4\n",
       "                                        ESCUELA                 4\n",
       "                                        ESTETICA                2\n",
       "                                        HOGAR                   9\n",
       "                                        HOTEL                  51\n",
       "                                        IMAGEN                  1\n",
       "                                        LIBRERIA               45\n",
       "                                        MISC                   51\n",
       "                                        MUEBLES                 3\n",
       "                                        REGALOS                 5\n",
       "                                        RESTAURANTE           309\n",
       "                                        ROPA                    9\n",
       "                                        SALIDAS                 4\n",
       "                                        SALUD                   6\n",
       "                                        SUPERMERCADO           35\n",
       "                                        TELECOM                 2\n",
       "                                        TREN                    3\n",
       "                                        VIAJE                   5\n",
       "Name: actividad, Length: 1552, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['nombre','actividad']).actividad.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>nombre</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>minuto</th>\n",
       "      <th>importe</th>\n",
       "      <th>comercio</th>\n",
       "      <th>actividad_completa</th>\n",
       "      <th>actividad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-12-09</td>\n",
       "      <td>9</td>\n",
       "      <td>51</td>\n",
       "      <td>759.919983</td>\n",
       "      <td>NEW PALACIO DE LOS LICORES</td>\n",
       "      <td>supermercados,ultramarinos, economatos</td>\n",
       "      <td>SUPERMERCADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>352</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2005-06-19</td>\n",
       "      <td>19</td>\n",
       "      <td>42</td>\n",
       "      <td>11.400000</td>\n",
       "      <td>EMBASSY  S A</td>\n",
       "      <td>supermercados,ultramarinos, economatos</td>\n",
       "      <td>SUPERMERCADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>529</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2006-10-10</td>\n",
       "      <td>16</td>\n",
       "      <td>46</td>\n",
       "      <td>88.610001</td>\n",
       "      <td>SEMON</td>\n",
       "      <td>supermercados,ultramarinos, economatos</td>\n",
       "      <td>SUPERMERCADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>560</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2006-12-24</td>\n",
       "      <td>13</td>\n",
       "      <td>10</td>\n",
       "      <td>217.380005</td>\n",
       "      <td>PASTELERIAS MALLORCA (KE)</td>\n",
       "      <td>supermercados,ultramarinos, economatos</td>\n",
       "      <td>SUPERMERCADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>577</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2007-01-20</td>\n",
       "      <td>18</td>\n",
       "      <td>25</td>\n",
       "      <td>31.250000</td>\n",
       "      <td>PASTELERIAS MALLORCA (KE)</td>\n",
       "      <td>supermercados,ultramarinos, economatos</td>\n",
       "      <td>SUPERMERCADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>804</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2007-12-24</td>\n",
       "      <td>19</td>\n",
       "      <td>52</td>\n",
       "      <td>259.019989</td>\n",
       "      <td>PASTELERIAS MALLORCA (KE)</td>\n",
       "      <td>hipermercados (tecnologia electronica)</td>\n",
       "      <td>SUPERMERCADO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1078</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2009-01-11</td>\n",
       "      <td>22</td>\n",
       "      <td>45</td>\n",
       "      <td>63.270000</td>\n",
       "      <td>MALLORCA</td>\n",
       "      <td>hipermercados (tecnologia electronica)</td>\n",
       "      <td>SUPERMERCADO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              nombre       fecha  hora  minuto     importe  \\\n",
       "152   Alberto Recarte García Andrade  2003-12-09     9      51  759.919983   \n",
       "352   Alberto Recarte García Andrade  2005-06-19    19      42   11.400000   \n",
       "529   Alberto Recarte García Andrade  2006-10-10    16      46   88.610001   \n",
       "560   Alberto Recarte García Andrade  2006-12-24    13      10  217.380005   \n",
       "577   Alberto Recarte García Andrade  2007-01-20    18      25   31.250000   \n",
       "804   Alberto Recarte García Andrade  2007-12-24    19      52  259.019989   \n",
       "1078  Alberto Recarte García Andrade  2009-01-11    22      45   63.270000   \n",
       "\n",
       "                            comercio                      actividad_completa  \\\n",
       "152   NEW PALACIO DE LOS LICORES      supermercados,ultramarinos, economatos   \n",
       "352   EMBASSY  S A                    supermercados,ultramarinos, economatos   \n",
       "529   SEMON                           supermercados,ultramarinos, economatos   \n",
       "560   PASTELERIAS MALLORCA (KE)       supermercados,ultramarinos, economatos   \n",
       "577   PASTELERIAS MALLORCA (KE)       supermercados,ultramarinos, economatos   \n",
       "804   PASTELERIAS MALLORCA (KE)       hipermercados (tecnologia electronica)   \n",
       "1078  MALLORCA                        hipermercados (tecnologia electronica)   \n",
       "\n",
       "         actividad  \n",
       "152   SUPERMERCADO  \n",
       "352   SUPERMERCADO  \n",
       "529   SUPERMERCADO  \n",
       "560   SUPERMERCADO  \n",
       "577   SUPERMERCADO  \n",
       "804   SUPERMERCADO  \n",
       "1078  SUPERMERCADO  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.nombre=='Alberto Recarte García Andrade') & (df.actividad=='SUPERMERCADO')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's do a new DataFrame with a summary of information:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_summary = pd.DataFrame(df.groupby('nombre').actividad.count())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>actividad</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nombre</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alberto Recarte García Andrade</th>\n",
       "      <td>1357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alejandro Couceiro Ojeda</th>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angel Rizaldos González</th>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antonio Cámara Eguinoa</th>\n",
       "      <td>3358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antonio Rey de Viñas Sánchez-Majestad</th>\n",
       "      <td>1822</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       actividad\n",
       "nombre                                          \n",
       "Alberto Recarte García Andrade              1357\n",
       "Alejandro Couceiro Ojeda                     549\n",
       "Angel Rizaldos González                      120\n",
       "Antonio Cámara Eguinoa                      3358\n",
       "Antonio Rey de Viñas Sánchez-Majestad       1822"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_summary['importe_media'] = df.groupby('nombre').importe.mean()\n",
    "df_summary['importe_max'] = df.groupby('nombre').importe.max()\n",
    "df_summary['importe_min'] = df.groupby('nombre').importe.min()\n",
    "df_summary['importe_total'] = df.groupby('nombre').importe.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>actividad</th>\n",
       "      <th>importe_media</th>\n",
       "      <th>importe_max</th>\n",
       "      <th>importe_min</th>\n",
       "      <th>importe_total</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nombre</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Alberto Recarte García Andrade</th>\n",
       "      <td>1357</td>\n",
       "      <td>100.592498</td>\n",
       "      <td>3509.199951</td>\n",
       "      <td>-401.250000</td>\n",
       "      <td>136504.019797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alejandro Couceiro Ojeda</th>\n",
       "      <td>549</td>\n",
       "      <td>124.762277</td>\n",
       "      <td>1150.000000</td>\n",
       "      <td>-100.800003</td>\n",
       "      <td>68494.490011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Angel Rizaldos González</th>\n",
       "      <td>120</td>\n",
       "      <td>168.135918</td>\n",
       "      <td>843.000000</td>\n",
       "      <td>24.450001</td>\n",
       "      <td>20176.310112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antonio Cámara Eguinoa</th>\n",
       "      <td>3358</td>\n",
       "      <td>52.461551</td>\n",
       "      <td>2742.000000</td>\n",
       "      <td>-328.420013</td>\n",
       "      <td>176165.889852</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antonio Rey de Viñas Sánchez-Majestad</th>\n",
       "      <td>1822</td>\n",
       "      <td>103.598222</td>\n",
       "      <td>1751.079956</td>\n",
       "      <td>-241.490005</td>\n",
       "      <td>188755.959932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       actividad  importe_media  importe_max  \\\n",
       "nombre                                                                         \n",
       "Alberto Recarte García Andrade              1357     100.592498  3509.199951   \n",
       "Alejandro Couceiro Ojeda                     549     124.762277  1150.000000   \n",
       "Angel Rizaldos González                      120     168.135918   843.000000   \n",
       "Antonio Cámara Eguinoa                      3358      52.461551  2742.000000   \n",
       "Antonio Rey de Viñas Sánchez-Majestad       1822     103.598222  1751.079956   \n",
       "\n",
       "                                       importe_min  importe_total  \n",
       "nombre                                                             \n",
       "Alberto Recarte García Andrade         -401.250000  136504.019797  \n",
       "Alejandro Couceiro Ojeda               -100.800003   68494.490011  \n",
       "Angel Rizaldos González                  24.450001   20176.310112  \n",
       "Antonio Cámara Eguinoa                 -328.420013  176165.889852  \n",
       "Antonio Rey de Viñas Sánchez-Majestad  -241.490005  188755.959932  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For DataFrame with multi-level index, return new DataFrame with labeling information in the columns under the index names, defaulting to ‘level_0’, ‘level_1’, etc. if any are None. For a standard index, the index name will be used (if set), otherwise a default ‘index’ or ‘level_0’ (if ‘index’ is already taken) will be used."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_summary = df_summary.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_summary = df_summary.sort_values('importe_total',ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>nombre</th>\n",
       "      <th>actividad</th>\n",
       "      <th>importe_media</th>\n",
       "      <th>importe_max</th>\n",
       "      <th>importe_min</th>\n",
       "      <th>importe_total</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ildefonso José Sánchez Barcoj</td>\n",
       "      <td>2737</td>\n",
       "      <td>208.598979</td>\n",
       "      <td>16921.759766</td>\n",
       "      <td>-9784.730469</td>\n",
       "      <td>572187.000182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>José Antonio Moral Santín</td>\n",
       "      <td>1215</td>\n",
       "      <td>368.534757</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>447769.729996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Ricardo Morado Iglesias</td>\n",
       "      <td>265</td>\n",
       "      <td>1675.468641</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>-304.000000</td>\n",
       "      <td>443999.189930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Matías Amat Roca</td>\n",
       "      <td>1036</td>\n",
       "      <td>414.355366</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>-7287.740234</td>\n",
       "      <td>429272.159650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Miguel Blesa de la Parra</td>\n",
       "      <td>1565</td>\n",
       "      <td>270.331303</td>\n",
       "      <td>13148.299805</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>423068.489459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nombre  actividad  importe_media   importe_max  \\\n",
       "27  Ildefonso José Sánchez Barcoj       2737     208.598979  16921.759766   \n",
       "34      José Antonio Moral Santín       1215     368.534757   1000.000000   \n",
       "75        Ricardo Morado Iglesias        265    1675.468641   6800.000000   \n",
       "58               Matías Amat Roca       1036     414.355366  15000.000000   \n",
       "60       Miguel Blesa de la Parra       1565     270.331303  13148.299805   \n",
       "\n",
       "    importe_min  importe_total  \n",
       "27 -9784.730469  572187.000182  \n",
       "34     1.200000  447769.729996  \n",
       "75  -304.000000  443999.189930  \n",
       "58 -7287.740234  429272.159650  \n",
       "60 -2000.000000  423068.489459  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ok... wow... how much did they spent??"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Which casinos were used?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Which person spent more money in total? How much was it?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. In which activity he spent the maximum import? Which was this import?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Which was the annual median of money spent per person?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Rename a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={'actividad_completa':'actividad_detalles'},inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>nombre</th>\n",
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       "      <th>hora</th>\n",
       "      <th>minuto</th>\n",
       "      <th>importe</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
       "      <td>30</td>\n",
       "      <td>38.700001</td>\n",
       "      <td>RCG OFICINA</td>\n",
       "      <td>confeccion textil en general</td>\n",
       "      <td>ROPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>MANZANIL AREA</td>\n",
       "      <td>hoteles,moteles,balnearios,campings rest</td>\n",
       "      <td>HOTEL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-05</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>95.620003</td>\n",
       "      <td>REST REAL C GOLF SOTOGRAN</td>\n",
       "      <td>restaurantes resto</td>\n",
       "      <td>RESTAURANTE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>49.130001</td>\n",
       "      <td>ESTACONES DE SERVICIO ML</td>\n",
       "      <td>gasolineras</td>\n",
       "      <td>COCHE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>13.940000</td>\n",
       "      <td>NISA (H 24) VINHOLAN  S.A.</td>\n",
       "      <td>gasolineras</td>\n",
       "      <td>COCHE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nombre       fecha  hora  minuto    importe  \\\n",
       "0  Alberto Recarte García Andrade  2003-01-04    12      30  38.700001   \n",
       "1  Alberto Recarte García Andrade  2003-01-04    12      32  14.600000   \n",
       "2  Alberto Recarte García Andrade  2003-01-05    19       7  95.620003   \n",
       "3  Alberto Recarte García Andrade  2003-01-08    15      31  49.130001   \n",
       "4  Alberto Recarte García Andrade  2003-01-08    16       5  13.940000   \n",
       "\n",
       "                         comercio                        actividad_detalles  \\\n",
       "0  RCG OFICINA                                 confeccion textil en general   \n",
       "1  MANZANIL AREA                   hoteles,moteles,balnearios,campings rest   \n",
       "2  REST REAL C GOLF SOTOGRAN                             restaurantes resto   \n",
       "3  ESTACONES DE SERVICIO ML                                     gasolineras   \n",
       "4  NISA (H 24) VINHOLAN  S.A.                                   gasolineras   \n",
       "\n",
       "     actividad  \n",
       "0         ROPA  \n",
       "1        HOTEL  \n",
       "2  RESTAURANTE  \n",
       "3        COCHE  \n",
       "4        COCHE  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copy the dataframe and change the name of all columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_english = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_english.columns = ['name','date','hour','minute','import','business','activity_full','activity']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>38.700001</td>\n",
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       "      <td>confeccion textil en general</td>\n",
       "      <td>ROPA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>14.600000</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-05</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>95.620003</td>\n",
       "      <td>REST REAL C GOLF SOTOGRAN</td>\n",
       "      <td>restaurantes resto</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>49.130001</td>\n",
       "      <td>ESTACONES DE SERVICIO ML</td>\n",
       "      <td>gasolineras</td>\n",
       "      <td>COCHE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>13.940000</td>\n",
       "      <td>NISA (H 24) VINHOLAN  S.A.</td>\n",
       "      <td>gasolineras</td>\n",
       "      <td>COCHE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                             name        date  hour  minute     import  \\\n",
       "0  Alberto Recarte García Andrade  2003-01-04    12      30  38.700001   \n",
       "1  Alberto Recarte García Andrade  2003-01-04    12      32  14.600000   \n",
       "2  Alberto Recarte García Andrade  2003-01-05    19       7  95.620003   \n",
       "3  Alberto Recarte García Andrade  2003-01-08    15      31  49.130001   \n",
       "4  Alberto Recarte García Andrade  2003-01-08    16       5  13.940000   \n",
       "\n",
       "                         business                             activity_full  \\\n",
       "0  RCG OFICINA                                 confeccion textil en general   \n",
       "1  MANZANIL AREA                   hoteles,moteles,balnearios,campings rest   \n",
       "2  REST REAL C GOLF SOTOGRAN                             restaurantes resto   \n",
       "3  ESTACONES DE SERVICIO ML                                     gasolineras   \n",
       "4  NISA (H 24) VINHOLAN  S.A.                                   gasolineras   \n",
       "\n",
       "      activity  \n",
       "0         ROPA  \n",
       "1        HOTEL  \n",
       "2  RESTAURANTE  \n",
       "3        COCHE  \n",
       "4        COCHE  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_english.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operate over columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's convert the import from euros to dolars with the rate conversion 1€ = 1.14$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"importe_dolar\"] = df[\"importe\"]*1.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>minuto</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
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       "      <td>38.700001</td>\n",
       "      <td>RCG OFICINA</td>\n",
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       "      <td>44.118001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-04</td>\n",
       "      <td>12</td>\n",
       "      <td>32</td>\n",
       "      <td>14.600000</td>\n",
       "      <td>MANZANIL AREA</td>\n",
       "      <td>hoteles,moteles,balnearios,campings rest</td>\n",
       "      <td>HOTEL</td>\n",
       "      <td>16.644000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-05</td>\n",
       "      <td>19</td>\n",
       "      <td>7</td>\n",
       "      <td>95.620003</td>\n",
       "      <td>REST REAL C GOLF SOTOGRAN</td>\n",
       "      <td>restaurantes resto</td>\n",
       "      <td>RESTAURANTE</td>\n",
       "      <td>109.006803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>15</td>\n",
       "      <td>31</td>\n",
       "      <td>49.130001</td>\n",
       "      <td>ESTACONES DE SERVICIO ML</td>\n",
       "      <td>gasolineras</td>\n",
       "      <td>COCHE</td>\n",
       "      <td>56.008201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alberto Recarte García Andrade</td>\n",
       "      <td>2003-01-08</td>\n",
       "      <td>16</td>\n",
       "      <td>5</td>\n",
       "      <td>13.940000</td>\n",
       "      <td>NISA (H 24) VINHOLAN  S.A.</td>\n",
       "      <td>gasolineras</td>\n",
       "      <td>COCHE</td>\n",
       "      <td>15.891600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nombre       fecha  hora  minuto    importe  \\\n",
       "0  Alberto Recarte García Andrade  2003-01-04    12      30  38.700001   \n",
       "1  Alberto Recarte García Andrade  2003-01-04    12      32  14.600000   \n",
       "2  Alberto Recarte García Andrade  2003-01-05    19       7  95.620003   \n",
       "3  Alberto Recarte García Andrade  2003-01-08    15      31  49.130001   \n",
       "4  Alberto Recarte García Andrade  2003-01-08    16       5  13.940000   \n",
       "\n",
       "                         comercio                        actividad_detalles  \\\n",
       "0  RCG OFICINA                                 confeccion textil en general   \n",
       "1  MANZANIL AREA                   hoteles,moteles,balnearios,campings rest   \n",
       "2  REST REAL C GOLF SOTOGRAN                             restaurantes resto   \n",
       "3  ESTACONES DE SERVICIO ML                                     gasolineras   \n",
       "4  NISA (H 24) VINHOLAN  S.A.                                   gasolineras   \n",
       "\n",
       "     actividad  importe_dolar  \n",
       "0         ROPA      44.118001  \n",
       "1        HOTEL      16.644000  \n",
       "2  RESTAURANTE     109.006803  \n",
       "3        COCHE      56.008201  \n",
       "4        COCHE      15.891600  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "total_money = df_summary.importe_total.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_summary[\"porcentaje\"] = (df_summary.importe_total / total_money)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nombre</th>\n",
       "      <th>actividad</th>\n",
       "      <th>importe_media</th>\n",
       "      <th>importe_max</th>\n",
       "      <th>importe_min</th>\n",
       "      <th>importe_total</th>\n",
       "      <th>porcentaje</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Ildefonso José Sánchez Barcoj</td>\n",
       "      <td>2737</td>\n",
       "      <td>208.598979</td>\n",
       "      <td>16921.759766</td>\n",
       "      <td>-9784.730469</td>\n",
       "      <td>572187.000182</td>\n",
       "      <td>4.846261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>José Antonio Moral Santín</td>\n",
       "      <td>1215</td>\n",
       "      <td>368.534757</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>447769.729996</td>\n",
       "      <td>3.792482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>Ricardo Morado Iglesias</td>\n",
       "      <td>265</td>\n",
       "      <td>1675.468641</td>\n",
       "      <td>6800.000000</td>\n",
       "      <td>-304.000000</td>\n",
       "      <td>443999.189930</td>\n",
       "      <td>3.760547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>Matías Amat Roca</td>\n",
       "      <td>1036</td>\n",
       "      <td>414.355366</td>\n",
       "      <td>15000.000000</td>\n",
       "      <td>-7287.740234</td>\n",
       "      <td>429272.159650</td>\n",
       "      <td>3.635813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>Miguel Blesa de la Parra</td>\n",
       "      <td>1565</td>\n",
       "      <td>270.331303</td>\n",
       "      <td>13148.299805</td>\n",
       "      <td>-2000.000000</td>\n",
       "      <td>423068.489459</td>\n",
       "      <td>3.583270</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           nombre  actividad  importe_media   importe_max  \\\n",
       "27  Ildefonso José Sánchez Barcoj       2737     208.598979  16921.759766   \n",
       "34      José Antonio Moral Santín       1215     368.534757   1000.000000   \n",
       "75        Ricardo Morado Iglesias        265    1675.468641   6800.000000   \n",
       "58               Matías Amat Roca       1036     414.355366  15000.000000   \n",
       "60       Miguel Blesa de la Parra       1565     270.331303  13148.299805   \n",
       "\n",
       "    importe_min  importe_total  porcentaje  \n",
       "27 -9784.730469  572187.000182    4.846261  \n",
       "34     1.200000  447769.729996    3.792482  \n",
       "75  -304.000000  443999.189930    3.760547  \n",
       "58 -7287.740234  429272.159650    3.635813  \n",
       "60 -2000.000000  423068.489459    3.583270  "
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_summary.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Operate over specific rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "66      direct marketing-combination catalog&amp;ret\n",
       "212     direct marketing-combination catalog&amp;ret\n",
       "213     direct marketing-combination catalog&amp;ret\n",
       "1923              men's &amp; ladies clothing stores\n",
       "2365    antique shops-sales, repairs &amp; restorati\n",
       "Name: actividad_detalles, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.actividad_detalles.notnull())&(df.actividad_detalles.str.contains('&amp;'))].actividad_detalles[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df.actividad_detalles.notnull())&(df.actividad_detalles.str.contains('&amp;')), 'actividad_detalles'] = df[(df.actividad_detalles.notnull())&(df.actividad_detalles.str.contains('&amp;'))].actividad_detalles.str.replace('&amp;','&')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>nombre</th>\n",
       "      <th>fecha</th>\n",
       "      <th>hora</th>\n",
       "      <th>minuto</th>\n",
       "      <th>importe</th>\n",
       "      <th>comercio</th>\n",
       "      <th>actividad_detalles</th>\n",
       "      <th>actividad</th>\n",
       "      <th>importe_dolar</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [nombre, fecha, hora, minuto, importe, comercio, actividad_detalles, actividad, importe_dolar]\n",
       "Index: []"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df.actividad_detalles.notnull())&(df.actividad_detalles.str.contains('&amp;'))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discretize_import(importe):\n",
    "    importe = abs(importe)\n",
    "    if importe <= 10:\n",
    "        return 'LOW'\n",
    "    elif 10 < importe <= 100:\n",
    "        return 'MEDIUM'\n",
    "    elif 100 <= importe < 1000:\n",
    "        return 'HIGH'\n",
    "    else:\n",
    "        return 'SUPER-HIGH'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['importe_discretizado'] = df.importe.apply(lambda x: discretize_import(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Check if what the press said is true"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Do you believe the quote from this [newspaper article](https://www.europapress.es/nacional/noticia-gastos-rato-blesa-tarjetas-vistazo-20141010175452.html)? \n",
    "\n",
    "\"Los apuntes incluidos en la documentación incluyen gastos como varios miles de euros en vino en el establecimiento Lavinia España Selección, durante la Navidad y otras ocasiones, de Miguel Blesa; o 1.849,23 euros en 'clubs, salas de fiestas, pubs, discotecas y bares' cuatro días seguidos, de Rodrigo Rato.\"  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Who spent money on McDonald's?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concat"
   ]
  },
  {
   "attachments": {
    "merging_concat_basic.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![merging_concat_basic.png](attachment:merging_concat_basic.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                    'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                    'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                    'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                    index=[0, 1, 2, 3])\n",
    "\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                    'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                    'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                    'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                    index=[4, 5, 6, 7])\n",
    "\n",
    "\n",
    "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
    "                   'B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                   'C': ['C8', 'C9', 'C10', 'C11'],\n",
    "                   'D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                   index=[8, 9, 10, 11])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B   C   D\n",
       "0  A0  B0  C0  D0\n",
       "1  A1  B1  C1  D1\n",
       "2  A2  B2  C2  D2\n",
       "3  A3  B3  C3  D3"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "    A   B   C   D\n",
       "4  A4  B4  C4  D4\n",
       "5  A5  B5  C5  D5\n",
       "6  A6  B6  C6  D6\n",
       "7  A7  B7  C7  D7"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
       "    </tr>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A4</td>\n",
       "      <td>B4</td>\n",
       "      <td>C4</td>\n",
       "      <td>D4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A5</td>\n",
       "      <td>B5</td>\n",
       "      <td>C5</td>\n",
       "      <td>D5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>A6</td>\n",
       "      <td>B6</td>\n",
       "      <td>C6</td>\n",
       "      <td>D6</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>A7</td>\n",
       "      <td>B7</td>\n",
       "      <td>C7</td>\n",
       "      <td>D7</td>\n",
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       "      <th>8</th>\n",
       "      <td>A8</td>\n",
       "      <td>B8</td>\n",
       "      <td>C8</td>\n",
       "      <td>D8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>A9</td>\n",
       "      <td>B9</td>\n",
       "      <td>C9</td>\n",
       "      <td>D9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D\n",
       "0    A0   B0   C0   D0\n",
       "1    A1   B1   C1   D1\n",
       "2    A2   B2   C2   D2\n",
       "3    A3   B3   C3   D3\n",
       "4    A4   B4   C4   D4\n",
       "5    A5   B5   C5   D5\n",
       "6    A6   B6   C6   D6\n",
       "7    A7   B7   C7   D7\n",
       "8    A8   B8   C8   D8\n",
       "9    A9   B9   C9   D9\n",
       "10  A10  B10  C10  D10\n",
       "11  A11  B11  C11  D11"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2, df3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df4 = pd.DataFrame({'B': ['B2', 'B3', 'B6', 'B7'],\n",
    "                    'D': ['D2', 'D3', 'D6', 'D7'],\n",
    "                    'F': ['F2', 'F3', 'F6', 'F7']},\n",
    "                    index=[2, 3, 6, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "concat() got an unexpected keyword argument 'sort'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-49-9a44d5706951>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msort\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: concat() got an unexpected keyword argument 'sort'"
     ]
    }
   ],
   "source": [
    "pd.concat([df1, df4], axis=1, sort=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.concat([df1, df4], axis=1, join='inner')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python\n",
    "pd.merge(left, right, \n",
    "        how='inner', \n",
    "        on=None, \n",
    "        left_on=None, \n",
    "        right_on=None,\n",
    "        left_index=False, \n",
    "        right_index=False```"
   ]
  },
  {
   "attachments": {
    "merge-pandas.png": {
     "image/png": 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    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![merge-pandas.png](attachment:merge-pandas.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n",
    "                     'key2': ['K0', 'K1', 'K0', 'K1'],\n",
    "                     'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                     'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "\n",
    "right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n",
    "                      'key2': ['K0', 'K0', 'K0', 'K0'],\n",
    "                      'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                      'D': ['D0', 'D1', 'D2', 'D3']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(left, right, on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(left, right, how='left', on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(left, right, how='right', on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.merge(left, right, how='outer', on=['key1', 'key2'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exercises"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have download the 'cargos_targetas_black.xlsx dataset from el [periodico](https://www.elperiodico.com/es/graficos/economia/imputados-tarjetas-black-caja-madrid-8550/). Please add it to the main dataframe and calculate how many money was spent by members or the same political party / labour union?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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