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{"cells": [{"cell_type": "code","execution_count": 3,"metadata": {"scrolled": true},"outputs": [{"name": "stdout","output_type": "stream","text": ["Collecting pandas\n"," Using cached pandas-0.20.3-cp36-cp36m-manylinux1_x86_64.whl\n","Collecting matplotlib\n"," Using cached matplotlib-2.0.2-cp36-cp36m-manylinux1_x86_64.whl\n","Collecting numpy\n"," Using cached numpy-1.13.1-cp36-cp36m-manylinux1_x86_64.whl\n","Collecting pytz>=2011k (from pandas)\n"," Using cached pytz-2017.2-py2.py3-none-any.whl\n","Requirement already satisfied: python-dateutil>=2 in ./lib/python3.6/site-packages (from pandas)\n","Collecting pyparsing!=2.0.0,!=2.0.4,!=2.1.2,!=2.1.6,>=1.5.6 (from matplotlib)\n"," Using cached pyparsing-2.2.0-py2.py3-none-any.whl\n","Requirement already satisfied: six>=1.10 in ./lib/python3.6/site-packages (from matplotlib)\n","Collecting cycler>=0.10 (from matplotlib)\n"," Using cached cycler-0.10.0-py2.py3-none-any.whl\n","Installing collected packages: pytz, numpy, pandas, pyparsing, cycler, matplotlib\n","Successfully installed cycler-0.10.0 matplotlib-2.0.2 numpy-1.13.1 pandas-0.20.3 pyparsing-2.2.0 pytz-2017.2\n"]}],"source": ["!pip install pandas matplotlib numpy"]},{"cell_type": "code","execution_count": 2,"metadata": {"collapsed": true},"outputs": [],"source": ["%matplotlib inline\n","import pandas as pd\n","import numpy as np"]},{"cell_type": "code","execution_count": 3,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": ["[[1, 2, 3], [4, 5, 6], [7, 8, 9]]\n"]},{"data": {"text/plain": ["[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"]},"execution_count": 3,"metadata": {},"output_type": "execute_result"}],"source": ["grid = [[1,2,3], [4,5,6], [7,8,9]]\n","print(grid)\n","\n","[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"]},{"cell_type": "code","execution_count": 4,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" 0 1 2\n","0 1 2 3\n","1 4 5 6\n","2 7 8 9\n"]}],"source": ["import pandas as pd\n","df = pd.DataFrame(grid)\n","print(df)"]},{"cell_type": "code","execution_count": 5,"metadata": {"scrolled": true},"outputs": [{"name": "stdout","output_type": "stream","text": [" one two three\n","0 1 2 3\n","1 4 5 6\n","2 7 8 9\n"]}],"source": ["df = pd.DataFrame(grid, columns=[\"one\", \"two\", \"three\"] )\n","print(df)\n"]},{"cell_type": "code","execution_count": 6,"metadata": {"scrolled": false},"outputs": [{"name": "stdout","output_type": "stream","text": ["0 2\n","1 5\n","2 8\n","Name: two, dtype: int64\n"]}],"source": ["print(df[\"two\"])"]},{"cell_type": "code","execution_count": 7,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": ["[2, 5, 8]\n"]},{"data": {"text/plain": ["[2, 5, 8]"]},"execution_count": 7,"metadata": {},"output_type": "execute_result"}],"source": ["print([x[1] for x in grid])\n","[2, 5, 8]"]},{"cell_type": "code","execution_count": 8,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": ["2\n","5\n","8\n"]}],"source": ["for x in df[\"two\"]:\n"," print(x)"]},{"cell_type": "code","execution_count": 9,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" one three\n","0 1 3\n","1 4 6\n","2 7 9\n"]}],"source": ["edges = df[[\"one\", \"three\"]]\n","print(edges)\n"]},{"cell_type": "code","execution_count": 10,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" one three\n","0 3 5\n","1 6 8\n","2 9 11\n"]}],"source": ["print(edges.add(2))"]},{"cell_type": "code","execution_count": 11,"metadata": {},"outputs": [{"data": {"text/plain": ["2 1\n","5 1\n","8 1\n","Name: two, dtype: int64"]},"execution_count": 11,"metadata": {},"output_type": "execute_result"}],"source": ["df['two'].value_counts()"]},{"cell_type": "code","execution_count": 18,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" report\n","abs_humidity None\n","atmo_opacity Sunny\n","ls 296\n","max_temp -1\n","max_temp_fahrenheit 30.2\n","min_temp -72\n","min_temp_fahrenheit -97.6\n","pressure 869\n","pressure_string Higher\n","season Month 10\n","sol 1576\n","sunrise 2017-01-11T12:31:00Z\n","sunset 2017-01-12T00:46:00Z\n","terrestrial_date 2017-01-11\n","wind_direction --\n","wind_speed None\n"]}],"source": ["mars = pd.read_json(\"mars_data_01.json\")\n","print(mars)"]},{"cell_type": "code","execution_count": 27,"metadata": {"collapsed": true},"outputs": [],"source": ["temp = pd.read_csv(\"temp_data_01.csv\", header=0, names=range(18), usecols=range(4,18))"]},{"cell_type": "code","execution_count": 28,"metadata": {"scrolled": true},"outputs": [{"name": "stdout","output_type": "stream","text": [" 4 5 6 7 8 9 10 11 12 13 14 \\\n","0 1979/01/01 17.48 994 6.0 30.5 2.89 994 -13.6 15.8 Missing 0 \n","1 1979/01/02 4.64 994 -6.4 15.8 -9.03 994 -23.6 6.6 Missing 0 \n","2 1979/01/03 11.05 994 -0.7 24.7 -2.17 994 -18.3 12.9 Missing 0 \n","3 1979/01/04 9.51 994 0.2 27.6 -0.43 994 -16.3 16.3 Missing 0 \n","4 1979/05/15 68.42 994 61.0 75.1 51.30 994 43.3 57.0 Missing 0 \n","5 1979/05/16 70.29 994 63.4 73.5 48.09 994 41.1 53.0 Missing 0 \n","6 1979/05/17 75.34 994 64.0 80.5 50.84 994 44.3 55.7 82.60 2 \n","7 1979/05/18 79.13 994 75.5 82.1 55.68 994 50.0 61.1 81.42 349 \n","8 1979/05/19 74.94 994 66.9 83.1 58.59 994 50.9 63.2 82.87 78 \n","\n"," 15 16 17 \n","0 Missing Missing 0.00% \n","1 Missing Missing 0.00% \n","2 Missing Missing 0.00% \n","3 Missing Missing 0.00% \n","4 Missing Missing 0.00% \n","5 Missing Missing 0.00% \n","6 82.40 82.80 0.20% \n","7 80.20 83.40 35.11% \n","8 81.60 85.20 7.85% \n"]}],"source": ["print(temp)"]},{"cell_type": "code","execution_count": 26,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" 4 5 6 7 8 9 10 11 12 13 14 \\\n","0 1979/01/01 17.48 994 6.0 30.5 2.89 994 -13.6 15.8 NaN 0 \n","1 1979/01/02 4.64 994 -6.4 15.8 -9.03 994 -23.6 6.6 NaN 0 \n","2 1979/01/03 11.05 994 -0.7 24.7 -2.17 994 -18.3 12.9 NaN 0 \n","3 1979/01/04 9.51 994 0.2 27.6 -0.43 994 -16.3 16.3 NaN 0 \n","4 1979/05/15 68.42 994 61.0 75.1 51.30 994 43.3 57.0 NaN 0 \n","5 1979/05/16 70.29 994 63.4 73.5 48.09 994 41.1 53.0 NaN 0 \n","6 1979/05/17 75.34 994 64.0 80.5 50.84 994 44.3 55.7 82.60 2 \n","7 1979/05/18 79.13 994 75.5 82.1 55.68 994 50.0 61.1 81.42 349 \n","8 1979/05/19 74.94 994 66.9 83.1 58.59 994 50.9 63.2 82.87 78 \n","\n"," 15 16 17 \n","0 NaN NaN 0.00% \n","1 NaN NaN 0.00% \n","2 NaN NaN 0.00% \n","3 NaN NaN 0.00% \n","4 NaN NaN 0.00% \n","5 NaN NaN 0.00% \n","6 82.4 82.8 0.20% \n","7 80.2 83.4 35.11% \n","8 81.6 85.2 7.85% \n"]}],"source": ["temp = pd.read_csv(\"temp_data_01.csv\", na_values=['Missing'], header=0, names=range(18), usecols=range(4,18))\n","print(temp)"]},{"cell_type": "code","execution_count": 29,"metadata": {},"outputs": [{"data": {"text/plain": ["'0.00%'"]},"execution_count": 29,"metadata": {},"output_type": "execute_result"}],"source": ["temp[17][0]"]},{"cell_type": "code","execution_count": 31,"metadata": {"scrolled": true},"outputs": [{"data": {"text/plain": ["'0.00'"]},"execution_count": 31,"metadata": {},"output_type": "execute_result"}],"source": ["temp[17]=temp[17].str.strip(\"%\")\n","temp[17][0]"]},{"cell_type": "code","execution_count": 34,"metadata": {},"outputs": [{"data": {"text/plain": ["'0.00'"]},"execution_count": 34,"metadata": {},"output_type": "execute_result"}],"source": ["temp[17][0]"]},{"cell_type": "code","execution_count": 32,"metadata": {},"outputs": [{"data": {"text/plain": ["0.0"]},"execution_count": 32,"metadata": {},"output_type": "execute_result"}],"source": ["temp[17] = pd.to_numeric(temp[17])\n","temp[17][0]"]},{"cell_type": "code","execution_count": 33,"metadata": {},"outputs": [{"data": {"text/plain": ["0 0.0000\n","1 0.0000\n","2 0.0000\n","3 0.0000\n","4 0.0000\n","5 0.0000\n","6 0.0020\n","7 0.3511\n","8 0.0785\n","Name: 17, dtype: float64"]},"execution_count": 33,"metadata": {},"output_type": "execute_result"}],"source": ["temp[17] = temp[17].div(100)\n","temp[17]"]},{"cell_type": "code","execution_count": 34,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" Team member Territory Month Calls\n","0 Jorge 3 1 107\n","1 Jorge 3 2 88\n","2 Jorge 3 3 84\n","3 Jorge 3 4 113\n","4 Ana 1 1 91\n","5 Ana 1 2 129\n","6 Ana 1 3 96\n","7 Ana 1 4 128\n","8 Ali 2 1 120\n","9 Ali 2 2 85\n","10 Ali 2 3 87\n","11 Ali 2 4 87\n"]}],"source": ["calls = pd.read_csv(\"sales_calls.csv\")\n","print(calls)"]},{"cell_type": "code","execution_count": 35,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" Territory Month Amount\n","0 1 1 54228\n","1 1 2 61640\n","2 1 3 43491\n","3 1 4 52173\n","4 2 1 36061\n","5 2 2 44957\n","6 2 3 35058\n","7 2 4 33855\n","8 3 1 50876\n","9 3 2 57682\n","10 3 3 53689\n","11 3 4 49173\n"]}],"source": ["revenue = pd.read_csv(\"sales_revenue.csv\")\n","print(revenue)"]},{"cell_type": "code","execution_count": 37,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" Team member Territory Month Calls Amount\n","0 Jorge 3 1 107 50876\n","1 Jorge 3 2 88 57682\n","2 Jorge 3 3 84 53689\n","3 Jorge 3 4 113 49173\n","4 Ana 1 1 91 54228\n","5 Ana 1 2 129 61640\n","6 Ana 1 3 96 43491\n","7 Ana 1 4 128 52173\n","8 Ali 2 1 120 36061\n","9 Ali 2 2 85 44957\n","10 Ali 2 3 87 35058\n","11 Ali 2 4 87 33855\n"]}],"source": ["calls_revenue = pd.merge(calls, revenue, on=['Territory', 'Month'])\n","print(calls_revenue)"]},{"cell_type": "code","execution_count": 38,"metadata": {"scrolled": true},"outputs": [{"name": "stdout","output_type": "stream","text": [" Team member Territory Month Calls Amount\n","0 Jorge 3 1 107 50876\n","1 Jorge 3 2 88 57682\n","2 Jorge 3 3 84 53689\n","3 Jorge 3 4 113 49173\n"]}],"source": ["print(calls_revenue[calls_revenue.Territory==3])"]},{"cell_type": "code","execution_count": 39,"metadata": {"scrolled": true},"outputs": [{"name": "stdout","output_type": "stream","text": [" Team member Territory Month Calls Amount\n","1 Jorge 3 2 88 57682\n","2 Jorge 3 3 84 53689\n","4 Ana 1 1 91 54228\n","9 Ali 2 2 85 44957\n"]}],"source": ["print(calls_revenue[calls_revenue.Amount/calls_revenue.Calls>500])"]},{"cell_type": "code","execution_count": 40,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" Team member Territory Month Calls Amount Call_Amount\n","0 Jorge 3 1 107 50876 475.476636\n","1 Jorge 3 2 88 57682 655.477273\n","2 Jorge 3 3 84 53689 639.154762\n","3 Jorge 3 4 113 49173 435.159292\n","4 Ana 1 1 91 54228 595.912088\n","5 Ana 1 2 129 61640 477.829457\n","6 Ana 1 3 96 43491 453.031250\n","7 Ana 1 4 128 52173 407.601562\n","8 Ali 2 1 120 36061 300.508333\n","9 Ali 2 2 85 44957 528.905882\n","10 Ali 2 3 87 35058 402.965517\n","11 Ali 2 4 87 33855 389.137931\n"]}],"source": ["calls_revenue['Call_Amount'] = calls_revenue.Amount/calls_revenue.Calls\n","print(calls_revenue)"]},{"cell_type": "code","execution_count": 42,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": ["1215\n","101.25\n","93.5\n","129\n","84\n"]}],"source": ["print(calls_revenue.Calls.sum())\n","print(calls_revenue.Calls.mean())\n","print(calls_revenue.Calls.median())\n","print(calls_revenue.Calls.max())\n","print(calls_revenue.Calls.min())"]},{"cell_type": "code","execution_count": 43,"metadata": {"scrolled": true},"outputs": [{"name": "stdout","output_type": "stream","text": [" Team member Territory Month Calls Amount Call_Amount\n","0 Jorge 3 1 107 50876 475.476636\n","1 Jorge 3 2 88 57682 655.477273\n","2 Jorge 3 3 84 53689 639.154762\n","4 Ana 1 1 91 54228 595.912088\n","5 Ana 1 2 129 61640 477.829457\n","9 Ali 2 2 85 44957 528.905882\n","464.2539427570093\n"]}],"source": ["print(calls_revenue[calls_revenue.Call_Amount >= calls_revenue.Call_Amount.median()])\n","print(calls_revenue.Call_Amount.median())\n"]},{"cell_type": "code","execution_count": 45,"metadata": {},"outputs": [{"name": "stdout","output_type": "stream","text": [" Calls Amount\n","Month \n","1 318 141165\n","2 302 164279\n","3 267 132238\n","4 328 135201\n"]}],"source": ["print(calls_revenue[['Month', 'Calls', 'Amount']].groupby(['Month']).sum())"]},{"cell_type": "code","execution_count": 46,"metadata": {"scrolled": true},"outputs": [{"name": "stdout","output_type": "stream","text": [" Calls Amount\n","Territory \n","1 444 211532\n","2 379 149931\n","3 392 211420\n"]}],"source": ["print(calls_revenue[['Territory', 'Calls', 'Amount']].groupby(['Territory']).sum())"]},{"cell_type": "code","execution_count": 47,"metadata": {"scrolled": true},"outputs": [{"data": {"text/plain": ["<matplotlib.axes._subplots.AxesSubplot at 0x7ff3576c9390>"]},"execution_count": 47,"metadata": {},"output_type": "execute_result"},{"data": {"image/png": 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["<matplotlib.figure.Figure at 0x7ff3578ceda0>"]},"metadata": {},"output_type": "display_data"}],"source": ["calls_revenue[['Territory', 'Calls']].groupby(['Territory']).sum().plot.bar()"]},{"cell_type": "code","execution_count": 152,"metadata": {},"outputs": [{"data": {"text/plain": ["<matplotlib.axes._subplots.AxesSubplot at 0x7fdee6e07cf8>"]},"execution_count": 152,"metadata": {},"output_type": "execute_result"},{"data": {"image/png": 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