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{
"cells": [
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"id": "pointed-civilization",
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{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
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{
"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",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Name</th>\n",
" <th>Platform</th>\n",
" <th>Year_of_Release</th>\n",
" <th>Genre</th>\n",
" <th>Publisher</th>\n",
" <th>NA_Sales</th>\n",
" <th>EU_Sales</th>\n",
" <th>JP_Sales</th>\n",
" <th>Other_Sales</th>\n",
" <th>Global_Sales</th>\n",
" <th>Critic_Score</th>\n",
" <th>Critic_Count</th>\n",
" <th>User_Score</th>\n",
" <th>User_Count</th>\n",
" <th>Developer</th>\n",
" <th>Rating</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Wii Sports</td>\n",
" <td>Wii</td>\n",
" <td>2006.0</td>\n",
" <td>Sports</td>\n",
" <td>Nintendo</td>\n",
" <td>41.36</td>\n",
" <td>28.96</td>\n",
" <td>3.77</td>\n",
" <td>8.45</td>\n",
" <td>82.53</td>\n",
" <td>76.0</td>\n",
" <td>51.0</td>\n",
" <td>8</td>\n",
" <td>322.0</td>\n",
" <td>Nintendo</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Super Mario Bros.</td>\n",
" <td>NES</td>\n",
" <td>1985.0</td>\n",
" <td>Platform</td>\n",
" <td>Nintendo</td>\n",
" <td>29.08</td>\n",
" <td>3.58</td>\n",
" <td>6.81</td>\n",
" <td>0.77</td>\n",
" <td>40.24</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Mario Kart Wii</td>\n",
" <td>Wii</td>\n",
" <td>2008.0</td>\n",
" <td>Racing</td>\n",
" <td>Nintendo</td>\n",
" <td>15.68</td>\n",
" <td>12.76</td>\n",
" <td>3.79</td>\n",
" <td>3.29</td>\n",
" <td>35.52</td>\n",
" <td>82.0</td>\n",
" <td>73.0</td>\n",
" <td>8.3</td>\n",
" <td>709.0</td>\n",
" <td>Nintendo</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Wii Sports Resort</td>\n",
" <td>Wii</td>\n",
" <td>2009.0</td>\n",
" <td>Sports</td>\n",
" <td>Nintendo</td>\n",
" <td>15.61</td>\n",
" <td>10.93</td>\n",
" <td>3.28</td>\n",
" <td>2.95</td>\n",
" <td>32.77</td>\n",
" <td>80.0</td>\n",
" <td>73.0</td>\n",
" <td>8</td>\n",
" <td>192.0</td>\n",
" <td>Nintendo</td>\n",
" <td>E</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Pokemon Red/Pokemon Blue</td>\n",
" <td>GB</td>\n",
" <td>1996.0</td>\n",
" <td>Role-Playing</td>\n",
" <td>Nintendo</td>\n",
" <td>11.27</td>\n",
" <td>8.89</td>\n",
" <td>10.22</td>\n",
" <td>1.00</td>\n",
" <td>31.37</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Name Platform Year_of_Release Genre Publisher \\\n",
"0 Wii Sports Wii 2006.0 Sports Nintendo \n",
"1 Super Mario Bros. NES 1985.0 Platform Nintendo \n",
"2 Mario Kart Wii Wii 2008.0 Racing Nintendo \n",
"3 Wii Sports Resort Wii 2009.0 Sports Nintendo \n",
"4 Pokemon Red/Pokemon Blue GB 1996.0 Role-Playing Nintendo \n",
"\n",
" NA_Sales EU_Sales JP_Sales Other_Sales Global_Sales Critic_Score \\\n",
"0 41.36 28.96 3.77 8.45 82.53 76.0 \n",
"1 29.08 3.58 6.81 0.77 40.24 NaN \n",
"2 15.68 12.76 3.79 3.29 35.52 82.0 \n",
"3 15.61 10.93 3.28 2.95 32.77 80.0 \n",
"4 11.27 8.89 10.22 1.00 31.37 NaN \n",
"\n",
" Critic_Count User_Score User_Count Developer Rating \n",
"0 51.0 8 322.0 Nintendo E \n",
"1 NaN NaN NaN NaN NaN \n",
"2 73.0 8.3 709.0 Nintendo E \n",
"3 73.0 8 192.0 Nintendo E \n",
"4 NaN NaN NaN NaN NaN "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_csv(r'/kaggle/input/video-game-sales-with-ratings/Video_Games_Sales_as_at_22_Dec_2016.csv')\n",
"df.head()"
]
},
{
"cell_type": "code",
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"id": "necessary-adams",
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"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 16719 entries, 0 to 16718\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Name 16717 non-null object \n",
" 1 Platform 16719 non-null object \n",
" 2 Year_of_Release 16450 non-null float64\n",
" 3 Genre 16717 non-null object \n",
" 4 Publisher 16665 non-null object \n",
" 5 NA_Sales 16719 non-null float64\n",
" 6 EU_Sales 16719 non-null float64\n",
" 7 JP_Sales 16719 non-null float64\n",
" 8 Other_Sales 16719 non-null float64\n",
" 9 Global_Sales 16719 non-null float64\n",
" 10 Critic_Score 8137 non-null float64\n",
" 11 Critic_Count 8137 non-null float64\n",
" 12 User_Score 10015 non-null object \n",
" 13 User_Count 7590 non-null float64\n",
" 14 Developer 10096 non-null object \n",
" 15 Rating 9950 non-null object \n",
"dtypes: float64(9), object(7)\n",
"memory usage: 2.0+ MB\n"
]
}
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"df.info()"
]
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{
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{
"cell_type": "code",
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"id": "boxed-quick",
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"outputs": [
{
"data": {
"text/plain": [
"array(['Sports', 'Platform', 'Racing', 'Role-Playing', 'Puzzle', 'Misc',\n",
" 'Shooter', 'Simulation', 'Action', 'Fighting', 'Adventure',\n",
" 'Strategy', nan], dtype=object)"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
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"df['Genre'].unique()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "referenced-housing",
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{
"data": {
"text/plain": [
"Action 3370\n",
"Sports 2348\n",
"Misc 1750\n",
"Role-Playing 1500\n",
"Shooter 1323\n",
"Adventure 1303\n",
"Racing 1249\n",
"Platform 888\n",
"Simulation 874\n",
"Fighting 849\n",
"Strategy 683\n",
"Puzzle 580\n",
"Name: Genre, dtype: int64"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df['Genre'].value_counts()"
]
},
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"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 16719 entries, 0 to 16718\n",
"Data columns (total 16 columns):\n",
" # Column Non-Null Count Dtype \n",
"--- ------ -------------- ----- \n",
" 0 Name 16717 non-null object \n",
" 1 Platform 16719 non-null object \n",
" 2 Year_of_Release 16450 non-null float64\n",
" 3 Genre 16717 non-null object \n",
" 4 Publisher 16665 non-null object \n",
" 5 NA_Sales 16719 non-null float64\n",
" 6 EU_Sales 16719 non-null float64\n",
" 7 JP_Sales 16719 non-null float64\n",
" 8 Other_Sales 16719 non-null float64\n",
" 9 Global_Sales 16719 non-null float64\n",
" 10 Critic_Score 8137 non-null float64\n",
" 11 Critic_Count 8137 non-null float64\n",
" 12 User_Score 10015 non-null object \n",
" 13 User_Count 7590 non-null float64\n",
" 14 Developer 10096 non-null object \n",
" 15 Rating 9950 non-null object \n",
"dtypes: float64(9), object(7)\n",
"memory usage: 2.0+ MB\n"
]
}
],
"source": [
"df.info()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "established-massachusetts",
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{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year_of_Release</th>\n",
" <th>NA_Sales</th>\n",
" <th>EU_Sales</th>\n",
" <th>JP_Sales</th>\n",
" <th>Other_Sales</th>\n",
" <th>Global_Sales</th>\n",
" <th>Critic_Score</th>\n",
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" <th>User_Count</th>\n",
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" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>16450.000000</td>\n",
" <td>16719.000000</td>\n",
" <td>16719.000000</td>\n",
" <td>16719.000000</td>\n",
" <td>16719.000000</td>\n",
" <td>16719.000000</td>\n",
" <td>8137.000000</td>\n",
" <td>8137.000000</td>\n",
" <td>7590.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2006.487356</td>\n",
" <td>0.263330</td>\n",
" <td>0.145025</td>\n",
" <td>0.077602</td>\n",
" <td>0.047332</td>\n",
" <td>0.533543</td>\n",
" <td>68.967679</td>\n",
" <td>26.360821</td>\n",
" <td>162.229908</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>5.878995</td>\n",
" <td>0.813514</td>\n",
" <td>0.503283</td>\n",
" <td>0.308818</td>\n",
" <td>0.186710</td>\n",
" <td>1.547935</td>\n",
" <td>13.938165</td>\n",
" <td>18.980495</td>\n",
" <td>561.282326</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1980.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>13.000000</td>\n",
" <td>3.000000</td>\n",
" <td>4.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2003.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.060000</td>\n",
" <td>60.000000</td>\n",
" <td>12.000000</td>\n",
" <td>10.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>2007.000000</td>\n",
" <td>0.080000</td>\n",
" <td>0.020000</td>\n",
" <td>0.000000</td>\n",
" <td>0.010000</td>\n",
" <td>0.170000</td>\n",
" <td>71.000000</td>\n",
" <td>21.000000</td>\n",
" <td>24.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>2010.000000</td>\n",
" <td>0.240000</td>\n",
" <td>0.110000</td>\n",
" <td>0.040000</td>\n",
" <td>0.030000</td>\n",
" <td>0.470000</td>\n",
" <td>79.000000</td>\n",
" <td>36.000000</td>\n",
" <td>81.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>2020.000000</td>\n",
" <td>41.360000</td>\n",
" <td>28.960000</td>\n",
" <td>10.220000</td>\n",
" <td>10.570000</td>\n",
" <td>82.530000</td>\n",
" <td>98.000000</td>\n",
" <td>113.000000</td>\n",
" <td>10665.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year_of_Release NA_Sales EU_Sales JP_Sales \\\n",
"count 16450.000000 16719.000000 16719.000000 16719.000000 \n",
"mean 2006.487356 0.263330 0.145025 0.077602 \n",
"std 5.878995 0.813514 0.503283 0.308818 \n",
"min 1980.000000 0.000000 0.000000 0.000000 \n",
"25% 2003.000000 0.000000 0.000000 0.000000 \n",
"50% 2007.000000 0.080000 0.020000 0.000000 \n",
"75% 2010.000000 0.240000 0.110000 0.040000 \n",
"max 2020.000000 41.360000 28.960000 10.220000 \n",
"\n",
" Other_Sales Global_Sales Critic_Score Critic_Count User_Count \n",
"count 16719.000000 16719.000000 8137.000000 8137.000000 7590.000000 \n",
"mean 0.047332 0.533543 68.967679 26.360821 162.229908 \n",
"std 0.186710 1.547935 13.938165 18.980495 561.282326 \n",
"min 0.000000 0.010000 13.000000 3.000000 4.000000 \n",
"25% 0.000000 0.060000 60.000000 12.000000 10.000000 \n",
"50% 0.010000 0.170000 71.000000 21.000000 24.000000 \n",
"75% 0.030000 0.470000 79.000000 36.000000 81.000000 \n",
"max 10.570000 82.530000 98.000000 113.000000 10665.000000 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.describe()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "armed-discrimination",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:32.625504Z",
"iopub.status.busy": "2021-04-04T17:06:32.624524Z",
"iopub.status.idle": "2021-04-04T17:06:32.628344Z",
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"exception": false,
"start_time": "2021-04-04T17:06:32.600810",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"a = df.groupby('Genre')['Global_Sales'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "promotional-prince",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:32.671586Z",
"iopub.status.busy": "2021-04-04T17:06:32.670617Z",
"iopub.status.idle": "2021-04-04T17:06:32.674309Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Genre\n",
"Action 1745.27\n",
"Sports 1332.00\n",
"Shooter 1052.94\n",
"Role-Playing 934.40\n",
"Platform 828.08\n",
"Misc 803.18\n",
"Racing 728.90\n",
"Fighting 447.48\n",
"Simulation 390.42\n",
"Puzzle 243.02\n",
"Adventure 237.69\n",
"Strategy 174.50\n",
"Name: Global_Sales, dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a.sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "athletic-melissa",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:32.715816Z",
"iopub.status.busy": "2021-04-04T17:06:32.714888Z",
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"exception": false,
"start_time": "2021-04-04T17:06:32.693626",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"b = df.groupby('Platform')['Global_Sales'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "irish-forth",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:32.768337Z",
"iopub.status.busy": "2021-04-04T17:06:32.767634Z",
"iopub.status.idle": "2021-04-04T17:06:32.771021Z",
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"exception": false,
"start_time": "2021-04-04T17:06:32.741377",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Platform\n",
"PS2 1255.64\n",
"X360 971.63\n",
"PS3 939.43\n",
"Wii 908.13\n",
"DS 807.10\n",
"PS 730.68\n",
"GBA 318.50\n",
"PS4 314.23\n",
"PSP 294.30\n",
"PC 260.30\n",
"3DS 259.09\n",
"XB 258.26\n",
"GB 255.45\n",
"NES 251.07\n",
"N64 218.88\n",
"SNES 200.05\n",
"GC 199.36\n",
"XOne 159.44\n",
"2600 97.08\n",
"WiiU 82.16\n",
"PSV 54.12\n",
"SAT 33.59\n",
"GEN 30.78\n",
"DC 15.97\n",
"SCD 1.87\n",
"NG 1.44\n",
"WS 1.42\n",
"TG16 0.16\n",
"3DO 0.10\n",
"GG 0.04\n",
"PCFX 0.03\n",
"Name: Global_Sales, dtype: float64"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"b.sort_values(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "fundamental-begin",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:32.813678Z",
"iopub.status.busy": "2021-04-04T17:06:32.812784Z",
"iopub.status.idle": "2021-04-04T17:06:32.837056Z",
"shell.execute_reply": "2021-04-04T17:06:32.837477Z"
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"end_time": "2021-04-04T17:06:32.837632",
"exception": false,
"start_time": "2021-04-04T17:06:32.790960",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"c = df.groupby('Name')['Global_Sales'].sum()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "digital-married",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:32.880952Z",
"iopub.status.busy": "2021-04-04T17:06:32.879963Z",
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"exception": false,
"start_time": "2021-04-04T17:06:32.856915",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Name\n",
"Fuuraiki 3 0.01\n",
"Unreal Tournament 2003 0.01\n",
"Game Book DS: Sword World 2.0 0.01\n",
"Bomberman (jp sales) 0.01\n",
"Kurogane Kaikitan 0.01\n",
" ... \n",
"Mario Kart Wii 35.52\n",
"Tetris 35.84\n",
"Super Mario Bros. 45.31\n",
"Grand Theft Auto V 56.57\n",
"Wii Sports 82.53\n",
"Name: Global_Sales, Length: 11562, dtype: float64"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"c.sort_values(ascending=True)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "fewer-necklace",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:32.934131Z",
"iopub.status.busy": "2021-04-04T17:06:32.933196Z",
"iopub.status.idle": "2021-04-04T17:06:32.944734Z",
"shell.execute_reply": "2021-04-04T17:06:32.944120Z"
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"start_time": "2021-04-04T17:06:32.909939",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"d = df.groupby(['Year_of_Release','Genre'])['Global_Sales'].sum().sort_values(ascending = False)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "floating-fossil",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:32.996983Z",
"iopub.status.busy": "2021-04-04T17:06:32.995978Z",
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"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"Year_of_Release Genre \n",
"2020.0 Simulation 0.29\n",
"2017.0 Role-Playing 0.05\n",
" Action 0.01\n",
"2016.0 Strategy 1.15\n",
" Sports 23.53\n",
" ... \n",
"1980.0 Sports 0.49\n",
" Shooter 7.07\n",
" Misc 2.71\n",
" Fighting 0.77\n",
" Action 0.34\n",
"Name: Global_Sales, Length: 390, dtype: float64"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d.sort_index(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "technological-composite",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:33.053323Z",
"iopub.status.busy": "2021-04-04T17:06:33.052493Z",
"iopub.status.idle": "2021-04-04T17:06:33.059952Z",
"shell.execute_reply": "2021-04-04T17:06:33.059306Z"
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"papermill": {
"duration": 0.035143,
"end_time": "2021-04-04T17:06:33.060094",
"exception": false,
"start_time": "2021-04-04T17:06:33.024951",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"e = df.groupby(['Year_of_Release','Genre'])[['NA_Sales','EU_Sales','JP_Sales','Other_Sales','Global_Sales']].sum()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "electrical-berlin",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:33.126809Z",
"iopub.status.busy": "2021-04-04T17:06:33.107298Z",
"iopub.status.idle": "2021-04-04T17:06:33.131310Z",
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"status": "completed"
},
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},
"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></th>\n",
" <th>NA_Sales</th>\n",
" <th>EU_Sales</th>\n",
" <th>JP_Sales</th>\n",
" <th>Other_Sales</th>\n",
" <th>Global_Sales</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Year_of_Release</th>\n",
" <th>Genre</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>2020.0</th>\n",
" <th>Simulation</th>\n",
" <td>0.27</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.02</td>\n",
" <td>0.29</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2017.0</th>\n",
" <th>Role-Playing</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.05</td>\n",
" <td>0.00</td>\n",
" <td>0.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Action</th>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"2\" valign=\"top\">2016.0</th>\n",
" <th>Strategy</th>\n",
" <td>0.24</td>\n",
" <td>0.59</td>\n",
" <td>0.23</td>\n",
" <td>0.07</td>\n",
" <td>1.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sports</th>\n",
" <td>7.54</td>\n",
" <td>12.01</td>\n",
" <td>0.92</td>\n",
" <td>3.02</td>\n",
" <td>23.53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th rowspan=\"5\" valign=\"top\">1980.0</th>\n",
" <th>Sports</th>\n",
" <td>0.46</td>\n",
" <td>0.03</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.49</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Shooter</th>\n",
" <td>6.56</td>\n",
" <td>0.43</td>\n",
" <td>0.00</td>\n",
" <td>0.08</td>\n",
" <td>7.07</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Misc</th>\n",
" <td>2.53</td>\n",
" <td>0.15</td>\n",
" <td>0.00</td>\n",
" <td>0.02</td>\n",
" <td>2.71</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Fighting</th>\n",
" <td>0.72</td>\n",
" <td>0.04</td>\n",
" <td>0.00</td>\n",
" <td>0.01</td>\n",
" <td>0.77</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Action</th>\n",
" <td>0.32</td>\n",
" <td>0.02</td>\n",
" <td>0.00</td>\n",
" <td>0.00</td>\n",
" <td>0.34</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>390 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" NA_Sales EU_Sales JP_Sales Other_Sales \\\n",
"Year_of_Release Genre \n",
"2020.0 Simulation 0.27 0.00 0.00 0.02 \n",
"2017.0 Role-Playing 0.00 0.00 0.05 0.00 \n",
" Action 0.00 0.00 0.01 0.00 \n",
"2016.0 Strategy 0.24 0.59 0.23 0.07 \n",
" Sports 7.54 12.01 0.92 3.02 \n",
"... ... ... ... ... \n",
"1980.0 Sports 0.46 0.03 0.00 0.01 \n",
" Shooter 6.56 0.43 0.00 0.08 \n",
" Misc 2.53 0.15 0.00 0.02 \n",
" Fighting 0.72 0.04 0.00 0.01 \n",
" Action 0.32 0.02 0.00 0.00 \n",
"\n",
" Global_Sales \n",
"Year_of_Release Genre \n",
"2020.0 Simulation 0.29 \n",
"2017.0 Role-Playing 0.05 \n",
" Action 0.01 \n",
"2016.0 Strategy 1.15 \n",
" Sports 23.53 \n",
"... ... \n",
"1980.0 Sports 0.49 \n",
" Shooter 7.07 \n",
" Misc 2.71 \n",
" Fighting 0.77 \n",
" Action 0.34 \n",
"\n",
"[390 rows x 5 columns]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"e.sort_index(ascending=False)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "lesser-outreach",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:06:33.178224Z",
"iopub.status.busy": "2021-04-04T17:06:33.177656Z",
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"start_time": "2021-04-04T17:06:33.152667",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "adapted-sauce",
"metadata": {
"papermill": {
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},
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"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "outstanding-fault",
"metadata": {
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"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
"mimetype": "text/x-python",
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| 0058/724/58724193.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "political-royalty",
"metadata": {
"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
"execution": {
"iopub.execute_input": "2021-04-04T17:10:47.585909Z",
"iopub.status.busy": "2021-04-04T17:10:47.585252Z",
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},
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"exception": false,
"start_time": "2021-04-04T17:10:47.570327",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"/kaggle/input/ico-coffee-dataset-worldwide/indicator-prices.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/disappearance.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/exports-calendar-year.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/prices-paid-to-growers.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/inventories.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/exports-crop-year.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/retail-prices.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/total-production.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/re-exports.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/imports.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/domestic-consumption.csv\n",
"/kaggle/input/ico-coffee-dataset-worldwide/gross-opening-stocks.csv\n"
]
}
],
"source": [
"# This Python 3 environment comes with many helpful analytics libraries installed\n",
"# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n",
"# For example, here's several helpful packages to load\n",
"\n",
"import numpy as np # linear algebra\n",
"import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
"\n",
"# Input data files are available in the read-only \"../input/\" directory\n",
"# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
"\n",
"import os\n",
"for dirname, _, filenames in os.walk('/kaggle/input'):\n",
" for filename in filenames:\n",
" print(os.path.join(dirname, filename))\n",
"\n",
"# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n",
"# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "cooperative-design",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:10:47.624708Z",
"iopub.status.busy": "2021-04-04T17:10:47.623944Z",
"iopub.status.idle": "2021-04-04T17:10:47.626364Z",
"shell.execute_reply": "2021-04-04T17:10:47.626904Z"
},
"papermill": {
"duration": 0.015961,
"end_time": "2021-04-04T17:10:47.627085",
"exception": false,
"start_time": "2021-04-04T17:10:47.611124",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df_paths=[\n",
" \"/kaggle/input/ico-coffee-dataset-worldwide/domestic-consumption.csv\",\n",
" \"/kaggle/input/ico-coffee-dataset-worldwide/exports-calendar-year.csv\",\n",
" \"/kaggle/input/ico-coffee-dataset-worldwide/exports-crop-year.csv\",\n",
" \"/kaggle/input/ico-coffee-dataset-worldwide/gross-opening-stocks.csv\",\n",
" \"/kaggle/input/ico-coffee-dataset-worldwide/total-production.csv\"\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "classified-treasurer",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:10:47.648296Z",
"iopub.status.busy": "2021-04-04T17:10:47.647633Z",
"iopub.status.idle": "2021-04-04T17:10:47.702850Z",
"shell.execute_reply": "2021-04-04T17:10:47.702119Z"
},
"papermill": {
"duration": 0.067396,
"end_time": "2021-04-04T17:10:47.703008",
"exception": false,
"start_time": "2021-04-04T17:10:47.635612",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"df=[pd.read_csv(df_path) for df_path in df_paths]"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "rising-withdrawal",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:10:47.740414Z",
"iopub.status.busy": "2021-04-04T17:10:47.731484Z",
"iopub.status.idle": "2021-04-04T17:10:47.822834Z",
"shell.execute_reply": "2021-04-04T17:10:47.823369Z"
},
"papermill": {
"duration": 0.112491,
"end_time": "2021-04-04T17:10:47.823573",
"exception": false,
"start_time": "2021-04-04T17:10:47.711082",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>exports</th>\n",
" <th>1990</th>\n",
" <th>1991</th>\n",
" <th>1992</th>\n",
" <th>1993</th>\n",
" <th>1994</th>\n",
" <th>1995</th>\n",
" <th>1996</th>\n",
" <th>1997</th>\n",
" <th>1998</th>\n",
" <th>...</th>\n",
" <th>2009</th>\n",
" <th>2010</th>\n",
" <th>2011</th>\n",
" <th>2012</th>\n",
" <th>2013</th>\n",
" <th>2014</th>\n",
" <th>2015</th>\n",
" <th>2016</th>\n",
" <th>2017</th>\n",
" <th>2018</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Angola</td>\n",
" <td>84.350000</td>\n",
" <td>70.501000</td>\n",
" <td>80.250000</td>\n",
" <td>38.878000</td>\n",
" <td>8.302000</td>\n",
" <td>40.559000</td>\n",
" <td>51.831000</td>\n",
" <td>50.494000</td>\n",
" <td>53.929000</td>\n",
" <td>...</td>\n",
" <td>6.925000</td>\n",
" <td>4.370000</td>\n",
" <td>7.575000</td>\n",
" <td>8.375000</td>\n",
" <td>5.520000</td>\n",
" <td>9.375000</td>\n",
" <td>10.515000</td>\n",
" <td>10.945000</td>\n",
" <td>9.055000</td>\n",
" <td>9.323397</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Benin</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.805000</td>\n",
" <td>0.050000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
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" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Bolivia (Plurinational State of)</td>\n",
" <td>156.442000</td>\n",
" <td>73.523000</td>\n",
" <td>96.204000</td>\n",
" <td>47.319000</td>\n",
" <td>84.321000</td>\n",
" <td>93.958000</td>\n",
" <td>123.445000</td>\n",
" <td>110.955000</td>\n",
" <td>97.039000</td>\n",
" <td>...</td>\n",
" <td>82.608773</td>\n",
" <td>78.268006</td>\n",
" <td>74.308883</td>\n",
" <td>62.675780</td>\n",
" <td>54.850533</td>\n",
" <td>61.751267</td>\n",
" <td>30.280158</td>\n",
" <td>22.456342</td>\n",
" <td>26.119992</td>\n",
" <td>22.459634</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Brazil</td>\n",
" <td>16935.787600</td>\n",
" <td>21182.761402</td>\n",
" <td>18790.719202</td>\n",
" <td>17837.747999</td>\n",
" <td>17273.147600</td>\n",
" <td>14468.432201</td>\n",
" <td>15250.609002</td>\n",
" <td>16801.260005</td>\n",
" <td>18144.388334</td>\n",
" <td>...</td>\n",
" <td>30377.981636</td>\n",
" <td>33166.641590</td>\n",
" <td>33806.009328</td>\n",
" <td>28549.425891</td>\n",
" <td>31650.562945</td>\n",
" <td>37335.172825</td>\n",
" <td>37562.846747</td>\n",
" <td>34269.150253</td>\n",
" <td>30924.567849</td>\n",
" <td>35382.556487</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Burundi</td>\n",
" <td>584.773000</td>\n",
" <td>687.851000</td>\n",
" <td>645.858000</td>\n",
" <td>417.609000</td>\n",
" <td>507.803000</td>\n",
" <td>528.202000</td>\n",
" <td>224.076000</td>\n",
" <td>528.764000</td>\n",
" <td>373.841000</td>\n",
" <td>...</td>\n",
" <td>288.830000</td>\n",
" <td>307.118958</td>\n",
" <td>217.845799</td>\n",
" <td>392.006917</td>\n",
" <td>194.715883</td>\n",
" <td>252.178000</td>\n",
" <td>230.188550</td>\n",
" <td>261.295433</td>\n",
" <td>168.876264</td>\n",
" <td>201.725236</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Cameroon</td>\n",
" <td>2611.259000</td>\n",
" <td>1752.179000</td>\n",
" <td>1645.851000</td>\n",
" <td>704.530000</td>\n",
" <td>545.889000</td>\n",
" <td>407.269000</td>\n",
" <td>563.549000</td>\n",
" <td>1368.030000</td>\n",
" <td>745.718000</td>\n",
" <td>...</td>\n",
" <td>617.757033</td>\n",
" <td>793.845667</td>\n",
" <td>490.283067</td>\n",
" <td>621.812800</td>\n",
" <td>271.949217</td>\n",
" <td>375.033867</td>\n",
" <td>390.142717</td>\n",
" <td>281.128967</td>\n",
" <td>245.017117</td>\n",
" <td>287.415250</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Central African Republic</td>\n",
" <td>196.698000</td>\n",
" <td>140.950000</td>\n",
" <td>99.975000</td>\n",
" <td>137.197000</td>\n",
" <td>136.676000</td>\n",
" <td>231.542000</td>\n",
" <td>98.328000</td>\n",
" <td>202.778000</td>\n",
" <td>102.320000</td>\n",
" <td>...</td>\n",
" <td>61.582000</td>\n",
" <td>95.194000</td>\n",
" <td>77.943000</td>\n",
" <td>77.692000</td>\n",
" <td>1.000000</td>\n",
" <td>75.027000</td>\n",
" <td>43.214000</td>\n",
" <td>80.018000</td>\n",
" <td>18.112667</td>\n",
" <td>38.528000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Colombia</td>\n",
" <td>13943.870000</td>\n",
" <td>12599.184998</td>\n",
" <td>16564.370001</td>\n",
" <td>13568.362004</td>\n",
" <td>11768.089000</td>\n",
" <td>9814.197000</td>\n",
" <td>10588.430998</td>\n",
" <td>10918.863002</td>\n",
" <td>11259.928999</td>\n",
" <td>...</td>\n",
" <td>7893.926795</td>\n",
" <td>7821.634504</td>\n",
" <td>7733.625254</td>\n",
" <td>7170.203291</td>\n",
" <td>9669.907367</td>\n",
" <td>10954.408357</td>\n",
" <td>12716.384670</td>\n",
" <td>12831.390727</td>\n",
" <td>12984.595747</td>\n",
" <td>12807.972625</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Congo</td>\n",
" <td>1.680000</td>\n",
" <td>0.924000</td>\n",
" <td>0.336000</td>\n",
" <td>0.504000</td>\n",
" <td>1.017000</td>\n",
" <td>19.149000</td>\n",
" <td>12.634000</td>\n",
" <td>2.670000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Costa Rica</td>\n",
" <td>2265.644000</td>\n",
" <td>2399.640000</td>\n",
" <td>2431.340001</td>\n",
" <td>2363.920000</td>\n",
" <td>2104.968000</td>\n",
" <td>2067.011000</td>\n",
" <td>2430.134000</td>\n",
" <td>2099.239000</td>\n",
" <td>2044.558001</td>\n",
" <td>...</td>\n",
" <td>1235.645154</td>\n",
" <td>1199.982617</td>\n",
" <td>1243.059935</td>\n",
" <td>1373.667164</td>\n",
" <td>1343.951587</td>\n",
" <td>1208.918498</td>\n",
" <td>1128.190321</td>\n",
" <td>1006.928354</td>\n",
" <td>987.154641</td>\n",
" <td>1207.946296</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Côte d'Ivoire</td>\n",
" <td>4282.866000</td>\n",
" <td>3804.734001</td>\n",
" <td>4546.368000</td>\n",
" <td>4033.107001</td>\n",
" <td>2443.993000</td>\n",
" <td>2493.946000</td>\n",
" <td>2750.602999</td>\n",
" <td>4712.982001</td>\n",
" <td>4365.288002</td>\n",
" <td>...</td>\n",
" <td>1806.525716</td>\n",
" <td>1912.053443</td>\n",
" <td>772.242167</td>\n",
" <td>1711.765617</td>\n",
" <td>1962.070767</td>\n",
" <td>1489.151150</td>\n",
" <td>1418.375167</td>\n",
" <td>1432.043600</td>\n",
" <td>854.620583</td>\n",
" <td>1522.457567</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Cuba</td>\n",
" <td>181.779000</td>\n",
" <td>121.429000</td>\n",
" <td>162.710000</td>\n",
" <td>116.195000</td>\n",
" <td>135.725000</td>\n",
" <td>122.287000</td>\n",
" <td>112.171000</td>\n",
" <td>106.663000</td>\n",
" <td>151.941000</td>\n",
" <td>...</td>\n",
" <td>7.462000</td>\n",
" <td>5.260000</td>\n",
" <td>9.755000</td>\n",
" <td>15.312000</td>\n",
" <td>12.415000</td>\n",
" <td>13.241000</td>\n",
" <td>12.796050</td>\n",
" <td>15.402100</td>\n",
" <td>15.126967</td>\n",
" <td>33.346885</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Democratic Republic of Congo</td>\n",
" <td>1838.636000</td>\n",
" <td>1417.957000</td>\n",
" <td>954.229000</td>\n",
" <td>919.506000</td>\n",
" <td>761.010000</td>\n",
" <td>1051.390000</td>\n",
" <td>881.561000</td>\n",
" <td>544.253000</td>\n",
" <td>640.369000</td>\n",
" <td>...</td>\n",
" <td>161.001733</td>\n",
" <td>162.109600</td>\n",
" <td>131.665067</td>\n",
" <td>146.383933</td>\n",
" <td>140.125333</td>\n",
" <td>150.577000</td>\n",
" <td>128.285000</td>\n",
" <td>170.955333</td>\n",
" <td>88.618991</td>\n",
" <td>187.999667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Dominican Republic</td>\n",
" <td>535.207000</td>\n",
" <td>476.263000</td>\n",
" <td>369.467001</td>\n",
" <td>331.432000</td>\n",
" <td>332.313000</td>\n",
" <td>451.018000</td>\n",
" <td>452.704000</td>\n",
" <td>306.977000</td>\n",
" <td>359.505001</td>\n",
" <td>...</td>\n",
" <td>102.396398</td>\n",
" <td>41.130774</td>\n",
" <td>88.597893</td>\n",
" <td>137.383017</td>\n",
" <td>64.723312</td>\n",
" <td>44.834272</td>\n",
" <td>19.143967</td>\n",
" <td>19.362536</td>\n",
" <td>24.396048</td>\n",
" <td>32.203177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Ecuador</td>\n",
" <td>1783.716000</td>\n",
" <td>1416.383999</td>\n",
" <td>1273.435001</td>\n",
" <td>1598.666001</td>\n",
" <td>2145.251000</td>\n",
" <td>1539.973999</td>\n",
" <td>1539.411000</td>\n",
" <td>1044.980999</td>\n",
" <td>1056.416000</td>\n",
" <td>...</td>\n",
" <td>1086.217400</td>\n",
" <td>1201.552727</td>\n",
" <td>1532.256606</td>\n",
" <td>1579.590942</td>\n",
" <td>1262.125910</td>\n",
" <td>1129.165184</td>\n",
" <td>869.048494</td>\n",
" <td>922.626339</td>\n",
" <td>695.146297</td>\n",
" <td>482.699333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>El Salvador</td>\n",
" <td>2509.873000</td>\n",
" <td>2148.210000</td>\n",
" <td>2120.092000</td>\n",
" <td>2947.071000</td>\n",
" <td>2092.955000</td>\n",
" <td>1807.498000</td>\n",
" <td>2314.147000</td>\n",
" <td>2772.126000</td>\n",
" <td>1684.247001</td>\n",
" <td>...</td>\n",
" <td>1309.200353</td>\n",
" <td>1081.766626</td>\n",
" <td>1826.306850</td>\n",
" <td>1043.986010</td>\n",
" <td>1102.743425</td>\n",
" <td>461.649110</td>\n",
" <td>575.864057</td>\n",
" <td>495.993015</td>\n",
" <td>527.101081</td>\n",
" <td>571.424066</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Equatorial Guinea</td>\n",
" <td>7.068000</td>\n",
" <td>3.321000</td>\n",
" <td>2.766000</td>\n",
" <td>2.463000</td>\n",
" <td>0.849000</td>\n",
" <td>3.385000</td>\n",
" <td>1.659000</td>\n",
" <td>1.120000</td>\n",
" <td>1.669000</td>\n",
" <td>...</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.058001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Ethiopia</td>\n",
" <td>1074.101000</td>\n",
" <td>841.329000</td>\n",
" <td>734.461000</td>\n",
" <td>1166.778000</td>\n",
" <td>1475.325000</td>\n",
" <td>1276.118000</td>\n",
" <td>1838.231000</td>\n",
" <td>1979.733000</td>\n",
" <td>1917.061000</td>\n",
" <td>...</td>\n",
" <td>1851.497453</td>\n",
" <td>3324.102551</td>\n",
" <td>2675.419013</td>\n",
" <td>3202.592407</td>\n",
" <td>2870.075248</td>\n",
" <td>3116.690370</td>\n",
" <td>2984.974817</td>\n",
" <td>3000.724450</td>\n",
" <td>3773.406333</td>\n",
" <td>3589.048020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Gabon</td>\n",
" <td>2.314000</td>\n",
" <td>3.338000</td>\n",
" <td>1.007000</td>\n",
" <td>2.178000</td>\n",
" <td>5.039000</td>\n",
" <td>2.946000</td>\n",
" <td>1.681000</td>\n",
" <td>0.000000</td>\n",
" <td>1.765000</td>\n",
" <td>...</td>\n",
" <td>0.675000</td>\n",
" <td>0.810000</td>\n",
" <td>0.752333</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.016813</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Ghana</td>\n",
" <td>10.865000</td>\n",
" <td>15.702000</td>\n",
" <td>32.245000</td>\n",
" <td>47.482000</td>\n",
" <td>10.815000</td>\n",
" <td>38.152000</td>\n",
" <td>4.359000</td>\n",
" <td>29.669000</td>\n",
" <td>17.787000</td>\n",
" <td>...</td>\n",
" <td>23.675383</td>\n",
" <td>6.621935</td>\n",
" <td>72.072736</td>\n",
" <td>30.467063</td>\n",
" <td>22.610512</td>\n",
" <td>26.441978</td>\n",
" <td>40.021570</td>\n",
" <td>26.801713</td>\n",
" <td>16.855640</td>\n",
" <td>12.400351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Guatemala</td>\n",
" <td>3240.141000</td>\n",
" <td>2831.985000</td>\n",
" <td>3327.988000</td>\n",
" <td>3750.687000</td>\n",
" <td>3274.069000</td>\n",
" <td>3700.872001</td>\n",
" <td>3978.998000</td>\n",
" <td>4243.882000</td>\n",
" <td>3541.853000</td>\n",
" <td>...</td>\n",
" <td>3492.821987</td>\n",
" <td>3468.087678</td>\n",
" <td>3696.825498</td>\n",
" <td>3750.112536</td>\n",
" <td>3575.011397</td>\n",
" <td>3043.438535</td>\n",
" <td>2960.862008</td>\n",
" <td>2991.120758</td>\n",
" <td>3382.666191</td>\n",
" <td>3327.489656</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Guinea</td>\n",
" <td>121.696000</td>\n",
" <td>61.447000</td>\n",
" <td>57.520000</td>\n",
" <td>15.469000</td>\n",
" <td>50.467000</td>\n",
" <td>151.552000</td>\n",
" <td>32.430000</td>\n",
" <td>114.344000</td>\n",
" <td>228.180000</td>\n",
" <td>...</td>\n",
" <td>438.785601</td>\n",
" <td>405.558528</td>\n",
" <td>384.523825</td>\n",
" <td>377.203544</td>\n",
" <td>134.277132</td>\n",
" <td>24.120806</td>\n",
" <td>151.692689</td>\n",
" <td>250.029166</td>\n",
" <td>215.691471</td>\n",
" <td>156.505008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Guyana</td>\n",
" <td>0.199999</td>\n",
" <td>0.421998</td>\n",
" <td>0.538998</td>\n",
" <td>0.052999</td>\n",
" <td>0.402997</td>\n",
" <td>0.025999</td>\n",
" <td>0.337558</td>\n",
" <td>0.323266</td>\n",
" <td>0.228302</td>\n",
" <td>...</td>\n",
" <td>1.367328</td>\n",
" <td>0.802501</td>\n",
" <td>0.590279</td>\n",
" <td>1.767020</td>\n",
" <td>1.110391</td>\n",
" <td>12.539913</td>\n",
" <td>1.976087</td>\n",
" <td>1.967133</td>\n",
" <td>0.811538</td>\n",
" <td>1.178945</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Haiti</td>\n",
" <td>191.445000</td>\n",
" <td>150.673000</td>\n",
" <td>165.976000</td>\n",
" <td>158.542000</td>\n",
" <td>103.468000</td>\n",
" <td>151.788000</td>\n",
" <td>156.468000</td>\n",
" <td>100.776000</td>\n",
" <td>127.306000</td>\n",
" <td>...</td>\n",
" <td>15.915874</td>\n",
" <td>8.740319</td>\n",
" <td>8.621106</td>\n",
" <td>11.061672</td>\n",
" <td>8.279471</td>\n",
" <td>3.141473</td>\n",
" <td>2.661146</td>\n",
" <td>1.277018</td>\n",
" <td>1.886211</td>\n",
" <td>2.019124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Honduras</td>\n",
" <td>1735.093000</td>\n",
" <td>1444.363000</td>\n",
" <td>1960.071000</td>\n",
" <td>1705.317000</td>\n",
" <td>1718.185000</td>\n",
" <td>1795.963000</td>\n",
" <td>2059.760000</td>\n",
" <td>1722.482000</td>\n",
" <td>2329.274000</td>\n",
" <td>...</td>\n",
" <td>3084.187828</td>\n",
" <td>3349.397639</td>\n",
" <td>3947.141151</td>\n",
" <td>5507.984939</td>\n",
" <td>4185.118924</td>\n",
" <td>4251.826667</td>\n",
" <td>5029.667006</td>\n",
" <td>5305.953308</td>\n",
" <td>7340.843952</td>\n",
" <td>7143.884471</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>India</td>\n",
" <td>1979.147999</td>\n",
" <td>1727.358999</td>\n",
" <td>1816.341000</td>\n",
" <td>2101.593000</td>\n",
" <td>2496.085000</td>\n",
" <td>2467.257001</td>\n",
" <td>3103.176000</td>\n",
" <td>2640.111001</td>\n",
" <td>3486.830003</td>\n",
" <td>...</td>\n",
" <td>3006.562463</td>\n",
" <td>4647.281111</td>\n",
" <td>5414.010577</td>\n",
" <td>5043.961929</td>\n",
" <td>5032.638529</td>\n",
" <td>5130.914270</td>\n",
" <td>5262.293887</td>\n",
" <td>6086.119737</td>\n",
" <td>6541.501049</td>\n",
" <td>5967.224604</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Indonesia</td>\n",
" <td>6903.226999</td>\n",
" <td>6170.796000</td>\n",
" <td>4603.696000</td>\n",
" <td>5662.094001</td>\n",
" <td>4604.629999</td>\n",
" <td>3946.585001</td>\n",
" <td>6440.280000</td>\n",
" <td>5755.078000</td>\n",
" <td>5598.031999</td>\n",
" <td>...</td>\n",
" <td>7907.273516</td>\n",
" <td>5489.147556</td>\n",
" <td>3919.891883</td>\n",
" <td>8205.740826</td>\n",
" <td>9254.807331</td>\n",
" <td>6174.809713</td>\n",
" <td>8378.687596</td>\n",
" <td>6545.392045</td>\n",
" <td>8197.573974</td>\n",
" <td>4538.839055</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Jamaica</td>\n",
" <td>14.984000</td>\n",
" <td>16.705000</td>\n",
" <td>22.427000</td>\n",
" <td>24.354000</td>\n",
" <td>15.826999</td>\n",
" <td>26.323000</td>\n",
" <td>27.285000</td>\n",
" <td>29.682000</td>\n",
" <td>16.981000</td>\n",
" <td>...</td>\n",
" <td>24.830517</td>\n",
" <td>15.434930</td>\n",
" <td>16.059855</td>\n",
" <td>14.919451</td>\n",
" <td>15.053724</td>\n",
" <td>10.248693</td>\n",
" <td>11.873981</td>\n",
" <td>11.223999</td>\n",
" <td>10.232406</td>\n",
" <td>9.751569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>Kenya</td>\n",
" <td>1969.338000</td>\n",
" <td>1557.532000</td>\n",
" <td>1384.291000</td>\n",
" <td>1438.799000</td>\n",
" <td>1358.411000</td>\n",
" <td>1450.046000</td>\n",
" <td>1901.584000</td>\n",
" <td>1159.095000</td>\n",
" <td>841.489000</td>\n",
" <td>...</td>\n",
" <td>524.748000</td>\n",
" <td>531.092657</td>\n",
" <td>609.163192</td>\n",
" <td>802.886282</td>\n",
" <td>814.542248</td>\n",
" <td>798.726789</td>\n",
" <td>712.056129</td>\n",
" <td>727.043152</td>\n",
" <td>709.638235</td>\n",
" <td>753.030915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Lao People's Democratic Republic</td>\n",
" <td>98.332999</td>\n",
" <td>98.332999</td>\n",
" <td>101.850000</td>\n",
" <td>141.667000</td>\n",
" <td>153.000000</td>\n",
" <td>165.232999</td>\n",
" <td>144.166999</td>\n",
" <td>110.083000</td>\n",
" <td>111.688000</td>\n",
" <td>...</td>\n",
" <td>263.861776</td>\n",
" <td>287.857831</td>\n",
" <td>420.965895</td>\n",
" <td>359.761897</td>\n",
" <td>399.651110</td>\n",
" <td>369.726347</td>\n",
" <td>403.169902</td>\n",
" <td>385.779741</td>\n",
" <td>385.587653</td>\n",
" <td>361.021635</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Liberia</td>\n",
" <td>22.411000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.244000</td>\n",
" <td>...</td>\n",
" <td>9.120000</td>\n",
" <td>7.216000</td>\n",
" <td>7.879316</td>\n",
" <td>2.810967</td>\n",
" <td>3.539333</td>\n",
" <td>1.345667</td>\n",
" <td>3.520000</td>\n",
" <td>2.955000</td>\n",
" <td>11.011667</td>\n",
" <td>7.733333</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Madagascar</td>\n",
" <td>863.040000</td>\n",
" <td>587.658000</td>\n",
" <td>736.406000</td>\n",
" <td>627.783000</td>\n",
" <td>435.621000</td>\n",
" <td>585.188000</td>\n",
" <td>718.049000</td>\n",
" <td>480.233000</td>\n",
" <td>796.116000</td>\n",
" <td>...</td>\n",
" <td>39.637137</td>\n",
" <td>73.584903</td>\n",
" <td>143.910720</td>\n",
" <td>78.917471</td>\n",
" <td>166.138449</td>\n",
" <td>137.809592</td>\n",
" <td>59.325518</td>\n",
" <td>53.302978</td>\n",
" <td>63.663211</td>\n",
" <td>6.475766</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Malawi</td>\n",
" <td>124.154000</td>\n",
" <td>99.535000</td>\n",
" <td>128.022000</td>\n",
" <td>103.574000</td>\n",
" <td>78.469000</td>\n",
" <td>91.633000</td>\n",
" <td>60.151000</td>\n",
" <td>60.170000</td>\n",
" <td>58.600000</td>\n",
" <td>...</td>\n",
" <td>17.575000</td>\n",
" <td>8.119890</td>\n",
" <td>25.853283</td>\n",
" <td>19.529999</td>\n",
" <td>28.729707</td>\n",
" <td>25.535307</td>\n",
" <td>17.859220</td>\n",
" <td>22.148678</td>\n",
" <td>12.622395</td>\n",
" <td>12.079999</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Mexico</td>\n",
" <td>3683.104002</td>\n",
" <td>3530.820002</td>\n",
" <td>3332.041000</td>\n",
" <td>3263.616001</td>\n",
" <td>2789.046000</td>\n",
" <td>3626.491999</td>\n",
" <td>4633.497001</td>\n",
" <td>4502.424001</td>\n",
" <td>3399.026001</td>\n",
" <td>...</td>\n",
" <td>2838.096450</td>\n",
" <td>2497.539985</td>\n",
" <td>2906.517574</td>\n",
" <td>3556.455805</td>\n",
" <td>3132.049713</td>\n",
" <td>2479.526809</td>\n",
" <td>2457.996501</td>\n",
" <td>2232.913073</td>\n",
" <td>2910.649964</td>\n",
" <td>2888.140482</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Nepal</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.008317</td>\n",
" <td>0.000000</td>\n",
" <td>0.250000</td>\n",
" <td>0.083333</td>\n",
" <td>0.066667</td>\n",
" <td>0.033333</td>\n",
" <td>...</td>\n",
" <td>1.508308</td>\n",
" <td>1.036625</td>\n",
" <td>3.748284</td>\n",
" <td>1.428570</td>\n",
" <td>1.220352</td>\n",
" <td>0.862922</td>\n",
" <td>2.024575</td>\n",
" <td>2.071278</td>\n",
" <td>0.974511</td>\n",
" <td>1.845253</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Nicaragua</td>\n",
" <td>671.184000</td>\n",
" <td>392.053000</td>\n",
" <td>635.946000</td>\n",
" <td>476.466000</td>\n",
" <td>623.514000</td>\n",
" <td>681.178999</td>\n",
" <td>822.356000</td>\n",
" <td>714.003000</td>\n",
" <td>940.583999</td>\n",
" <td>...</td>\n",
" <td>1373.999130</td>\n",
" <td>1711.816058</td>\n",
" <td>1467.709799</td>\n",
" <td>1987.302147</td>\n",
" <td>1660.549988</td>\n",
" <td>1900.637384</td>\n",
" <td>1753.158784</td>\n",
" <td>1963.467307</td>\n",
" <td>2448.637930</td>\n",
" <td>2300.035032</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Nigeria</td>\n",
" <td>3.582000</td>\n",
" <td>0.922000</td>\n",
" <td>11.391000</td>\n",
" <td>10.304000</td>\n",
" <td>7.873000</td>\n",
" <td>15.191000</td>\n",
" <td>6.924000</td>\n",
" <td>6.905000</td>\n",
" <td>9.897000</td>\n",
" <td>...</td>\n",
" <td>0.642488</td>\n",
" <td>3.044153</td>\n",
" <td>6.553999</td>\n",
" <td>1.662009</td>\n",
" <td>0.721269</td>\n",
" <td>1.432306</td>\n",
" <td>2.862852</td>\n",
" <td>1.400562</td>\n",
" <td>2.579840</td>\n",
" <td>3.012917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>Panama</td>\n",
" <td>131.654000</td>\n",
" <td>124.200000</td>\n",
" <td>125.404000</td>\n",
" <td>129.286000</td>\n",
" <td>76.486000</td>\n",
" <td>180.533000</td>\n",
" <td>138.598000</td>\n",
" <td>124.107000</td>\n",
" <td>145.123000</td>\n",
" <td>...</td>\n",
" <td>58.994871</td>\n",
" <td>65.335326</td>\n",
" <td>53.088844</td>\n",
" <td>50.592833</td>\n",
" <td>48.549188</td>\n",
" <td>51.024794</td>\n",
" <td>49.714221</td>\n",
" <td>55.585032</td>\n",
" <td>50.141141</td>\n",
" <td>46.893515</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Papua New Guinea</td>\n",
" <td>1050.719000</td>\n",
" <td>788.353000</td>\n",
" <td>917.174000</td>\n",
" <td>1054.634000</td>\n",
" <td>1156.837000</td>\n",
" <td>1002.052000</td>\n",
" <td>1090.076000</td>\n",
" <td>1043.437000</td>\n",
" <td>1348.968000</td>\n",
" <td>...</td>\n",
" <td>1026.972830</td>\n",
" <td>929.341207</td>\n",
" <td>1224.522669</td>\n",
" <td>924.930099</td>\n",
" <td>810.932640</td>\n",
" <td>806.833940</td>\n",
" <td>711.482100</td>\n",
" <td>1133.180307</td>\n",
" <td>794.100395</td>\n",
" <td>869.945983</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Paraguay</td>\n",
" <td>262.502000</td>\n",
" <td>120.084000</td>\n",
" <td>40.369001</td>\n",
" <td>58.442000</td>\n",
" <td>54.986000</td>\n",
" <td>14.006999</td>\n",
" <td>14.794000</td>\n",
" <td>9.703000</td>\n",
" <td>41.266000</td>\n",
" <td>...</td>\n",
" <td>0.018460</td>\n",
" <td>0.195814</td>\n",
" <td>0.019942</td>\n",
" <td>0.006549</td>\n",
" <td>0.009527</td>\n",
" <td>0.072768</td>\n",
" <td>0.000000</td>\n",
" <td>0.001983</td>\n",
" <td>0.010452</td>\n",
" <td>0.013536</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Peru</td>\n",
" <td>1105.078000</td>\n",
" <td>1041.867000</td>\n",
" <td>1061.043000</td>\n",
" <td>775.000000</td>\n",
" <td>1057.017000</td>\n",
" <td>1760.467000</td>\n",
" <td>1679.374000</td>\n",
" <td>1647.733000</td>\n",
" <td>1949.254000</td>\n",
" <td>...</td>\n",
" <td>3073.578000</td>\n",
" <td>3816.671040</td>\n",
" <td>4697.069665</td>\n",
" <td>4310.351922</td>\n",
" <td>3736.075980</td>\n",
" <td>2719.889850</td>\n",
" <td>2789.839140</td>\n",
" <td>3959.583593</td>\n",
" <td>3945.823228</td>\n",
" <td>4063.938661</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Philippines</td>\n",
" <td>168.282001</td>\n",
" <td>103.025000</td>\n",
" <td>41.316000</td>\n",
" <td>34.758000</td>\n",
" <td>125.669999</td>\n",
" <td>57.324001</td>\n",
" <td>27.032000</td>\n",
" <td>30.928999</td>\n",
" <td>21.003000</td>\n",
" <td>...</td>\n",
" <td>6.731293</td>\n",
" <td>6.366546</td>\n",
" <td>10.326075</td>\n",
" <td>3.090991</td>\n",
" <td>6.092139</td>\n",
" <td>12.037538</td>\n",
" <td>14.831043</td>\n",
" <td>12.943474</td>\n",
" <td>8.548641</td>\n",
" <td>3.992724</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Rwanda</td>\n",
" <td>779.150000</td>\n",
" <td>473.980000</td>\n",
" <td>644.525000</td>\n",
" <td>480.725000</td>\n",
" <td>30.024000</td>\n",
" <td>313.899000</td>\n",
" <td>265.306000</td>\n",
" <td>217.949000</td>\n",
" <td>214.272000</td>\n",
" <td>...</td>\n",
" <td>304.316256</td>\n",
" <td>295.962933</td>\n",
" <td>276.811683</td>\n",
" <td>250.844586</td>\n",
" <td>254.266747</td>\n",
" <td>255.682678</td>\n",
" <td>262.683550</td>\n",
" <td>244.335462</td>\n",
" <td>245.941270</td>\n",
" <td>265.649490</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Sierra Leone</td>\n",
" <td>139.744000</td>\n",
" <td>103.821000</td>\n",
" <td>47.020000</td>\n",
" <td>40.986000</td>\n",
" <td>61.924000</td>\n",
" <td>71.475000</td>\n",
" <td>28.512000</td>\n",
" <td>46.943000</td>\n",
" <td>41.865000</td>\n",
" <td>...</td>\n",
" <td>103.733457</td>\n",
" <td>57.576486</td>\n",
" <td>37.387028</td>\n",
" <td>65.270074</td>\n",
" <td>56.991638</td>\n",
" <td>24.031958</td>\n",
" <td>54.039745</td>\n",
" <td>25.806877</td>\n",
" <td>34.973872</td>\n",
" <td>26.546347</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Sri Lanka</td>\n",
" <td>19.137000</td>\n",
" <td>22.168000</td>\n",
" <td>14.724000</td>\n",
" <td>14.464000</td>\n",
" <td>47.359000</td>\n",
" <td>21.896000</td>\n",
" <td>11.536000</td>\n",
" <td>21.136000</td>\n",
" <td>27.266000</td>\n",
" <td>...</td>\n",
" <td>0.649418</td>\n",
" <td>1.655244</td>\n",
" <td>2.354069</td>\n",
" <td>0.152959</td>\n",
" <td>0.280154</td>\n",
" <td>2.338358</td>\n",
" <td>0.482531</td>\n",
" <td>1.449361</td>\n",
" <td>1.689305</td>\n",
" <td>1.537915</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Tanzania</td>\n",
" <td>1019.052000</td>\n",
" <td>871.329999</td>\n",
" <td>827.786000</td>\n",
" <td>1037.295999</td>\n",
" <td>632.137999</td>\n",
" <td>744.652999</td>\n",
" <td>969.637000</td>\n",
" <td>655.166999</td>\n",
" <td>742.305000</td>\n",
" <td>...</td>\n",
" <td>1156.944317</td>\n",
" <td>555.801933</td>\n",
" <td>798.099933</td>\n",
" <td>756.221317</td>\n",
" <td>934.794780</td>\n",
" <td>717.956937</td>\n",
" <td>708.912463</td>\n",
" <td>904.797654</td>\n",
" <td>664.079320</td>\n",
" <td>856.004136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Thailand</td>\n",
" <td>1001.435000</td>\n",
" <td>540.937000</td>\n",
" <td>1163.776001</td>\n",
" <td>981.782000</td>\n",
" <td>1136.046000</td>\n",
" <td>1209.523000</td>\n",
" <td>872.686000</td>\n",
" <td>1066.230000</td>\n",
" <td>778.031000</td>\n",
" <td>...</td>\n",
" <td>185.387559</td>\n",
" <td>369.627534</td>\n",
" <td>242.753954</td>\n",
" <td>349.719366</td>\n",
" <td>49.307703</td>\n",
" <td>27.370462</td>\n",
" <td>578.329072</td>\n",
" <td>130.952572</td>\n",
" <td>141.126331</td>\n",
" <td>258.133657</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Timor-Leste</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>...</td>\n",
" <td>43.197228</td>\n",
" <td>68.039268</td>\n",
" <td>38.721169</td>\n",
" <td>55.877050</td>\n",
" <td>70.239917</td>\n",
" <td>111.062363</td>\n",
" <td>67.686005</td>\n",
" <td>67.853308</td>\n",
" <td>86.277889</td>\n",
" <td>128.668467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Togo</td>\n",
" <td>238.838000</td>\n",
" <td>154.834000</td>\n",
" <td>313.177000</td>\n",
" <td>218.284000</td>\n",
" <td>165.795000</td>\n",
" <td>200.316000</td>\n",
" <td>85.490000</td>\n",
" <td>313.137000</td>\n",
" <td>165.801000</td>\n",
" <td>...</td>\n",
" <td>151.703783</td>\n",
" <td>198.107700</td>\n",
" <td>155.084167</td>\n",
" <td>147.390447</td>\n",
" <td>90.756533</td>\n",
" <td>133.968633</td>\n",
" <td>178.104817</td>\n",
" <td>74.876833</td>\n",
" <td>27.328767</td>\n",
" <td>111.044350</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Trinidad & Tobago</td>\n",
" <td>30.232000</td>\n",
" <td>7.671998</td>\n",
" <td>2.451000</td>\n",
" <td>9.744000</td>\n",
" <td>5.399998</td>\n",
" <td>4.059998</td>\n",
" <td>3.756998</td>\n",
" <td>3.951999</td>\n",
" <td>5.218999</td>\n",
" <td>...</td>\n",
" <td>0.143219</td>\n",
" <td>0.500076</td>\n",
" <td>0.299840</td>\n",
" <td>1.448238</td>\n",
" <td>2.485916</td>\n",
" <td>2.137128</td>\n",
" <td>1.932274</td>\n",
" <td>1.764547</td>\n",
" <td>1.800000</td>\n",
" <td>2.870569</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Uganda</td>\n",
" <td>2352.680000</td>\n",
" <td>2095.093000</td>\n",
" <td>1961.034000</td>\n",
" <td>1829.081000</td>\n",
" <td>3368.765000</td>\n",
" <td>3079.261000</td>\n",
" <td>4654.503000</td>\n",
" <td>3501.942000</td>\n",
" <td>3286.012000</td>\n",
" <td>...</td>\n",
" <td>3014.351000</td>\n",
" <td>2656.536000</td>\n",
" <td>3142.011000</td>\n",
" <td>2685.237000</td>\n",
" <td>3671.879000</td>\n",
" <td>3442.351000</td>\n",
" <td>3595.607000</td>\n",
" <td>3543.097000</td>\n",
" <td>4774.033000</td>\n",
" <td>4223.182000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>Venezuela</td>\n",
" <td>277.795000</td>\n",
" <td>127.889000</td>\n",
" <td>135.818001</td>\n",
" <td>525.002000</td>\n",
" <td>385.996000</td>\n",
" <td>96.752000</td>\n",
" <td>494.567000</td>\n",
" <td>104.537000</td>\n",
" <td>267.563000</td>\n",
" <td>...</td>\n",
" <td>18.181522</td>\n",
" <td>19.084311</td>\n",
" <td>0.467082</td>\n",
" <td>2.314776</td>\n",
" <td>5.763470</td>\n",
" <td>0.082086</td>\n",
" <td>0.503640</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>71.772996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>Viet Nam</td>\n",
" <td>1145.234001</td>\n",
" <td>1200.952000</td>\n",
" <td>1937.611000</td>\n",
" <td>2071.837000</td>\n",
" <td>2720.911000</td>\n",
" <td>3546.405000</td>\n",
" <td>3779.415000</td>\n",
" <td>6177.834000</td>\n",
" <td>6466.712000</td>\n",
" <td>...</td>\n",
" <td>17051.734477</td>\n",
" <td>14228.585796</td>\n",
" <td>17717.394731</td>\n",
" <td>22919.663832</td>\n",
" <td>19717.764414</td>\n",
" <td>26097.144160</td>\n",
" <td>20654.510096</td>\n",
" <td>27568.141684</td>\n",
" <td>22439.364593</td>\n",
" <td>27866.141588</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Yemen</td>\n",
" <td>50.000000</td>\n",
" <td>71.666997</td>\n",
" <td>36.989002</td>\n",
" <td>50.853002</td>\n",
" <td>92.840004</td>\n",
" <td>76.874003</td>\n",
" <td>42.007002</td>\n",
" <td>79.789003</td>\n",
" <td>70.790999</td>\n",
" <td>...</td>\n",
" <td>39.363849</td>\n",
" <td>43.530425</td>\n",
" <td>35.276688</td>\n",
" <td>62.651596</td>\n",
" <td>55.349134</td>\n",
" <td>56.989046</td>\n",
" <td>52.232413</td>\n",
" <td>37.901260</td>\n",
" <td>46.422101</td>\n",
" <td>42.118131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Zambia</td>\n",
" <td>24.360000</td>\n",
" <td>29.198000</td>\n",
" <td>30.514000</td>\n",
" <td>23.072000</td>\n",
" <td>23.954000</td>\n",
" <td>20.742000</td>\n",
" <td>35.824000</td>\n",
" <td>38.890000</td>\n",
" <td>33.308000</td>\n",
" <td>...</td>\n",
" <td>32.547667</td>\n",
" <td>17.871333</td>\n",
" <td>9.003000</td>\n",
" <td>7.075727</td>\n",
" <td>10.254890</td>\n",
" <td>5.244187</td>\n",
" <td>2.939550</td>\n",
" <td>9.791119</td>\n",
" <td>12.568981</td>\n",
" <td>26.168988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>Zimbabwe</td>\n",
" <td>250.772000</td>\n",
" <td>239.324000</td>\n",
" <td>162.318000</td>\n",
" <td>69.828000</td>\n",
" <td>31.744000</td>\n",
" <td>156.975000</td>\n",
" <td>150.362000</td>\n",
" <td>109.008000</td>\n",
" <td>153.268000</td>\n",
" <td>...</td>\n",
" <td>18.478566</td>\n",
" <td>7.434416</td>\n",
" <td>4.562206</td>\n",
" <td>4.003538</td>\n",
" <td>2.180804</td>\n",
" <td>11.302193</td>\n",
" <td>11.069010</td>\n",
" <td>12.287078</td>\n",
" <td>2.349193</td>\n",
" <td>4.805310</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>56 rows × 30 columns</p>\n",
"</div>"
],
"text/plain": [
" exports 1990 1991 \\\n",
"0 Angola 84.350000 70.501000 \n",
"1 Benin 0.000000 0.000000 \n",
"2 Bolivia (Plurinational State of) 156.442000 73.523000 \n",
"3 Brazil 16935.787600 21182.761402 \n",
"4 Burundi 584.773000 687.851000 \n",
"5 Cameroon 2611.259000 1752.179000 \n",
"6 Central African Republic 196.698000 140.950000 \n",
"7 Colombia 13943.870000 12599.184998 \n",
"8 Congo 1.680000 0.924000 \n",
"9 Costa Rica 2265.644000 2399.640000 \n",
"10 Côte d'Ivoire 4282.866000 3804.734001 \n",
"11 Cuba 181.779000 121.429000 \n",
"12 Democratic Republic of Congo 1838.636000 1417.957000 \n",
"13 Dominican Republic 535.207000 476.263000 \n",
"14 Ecuador 1783.716000 1416.383999 \n",
"15 El Salvador 2509.873000 2148.210000 \n",
"16 Equatorial Guinea 7.068000 3.321000 \n",
"17 Ethiopia 1074.101000 841.329000 \n",
"18 Gabon 2.314000 3.338000 \n",
"19 Ghana 10.865000 15.702000 \n",
"20 Guatemala 3240.141000 2831.985000 \n",
"21 Guinea 121.696000 61.447000 \n",
"22 Guyana 0.199999 0.421998 \n",
"23 Haiti 191.445000 150.673000 \n",
"24 Honduras 1735.093000 1444.363000 \n",
"25 India 1979.147999 1727.358999 \n",
"26 Indonesia 6903.226999 6170.796000 \n",
"27 Jamaica 14.984000 16.705000 \n",
"28 Kenya 1969.338000 1557.532000 \n",
"29 Lao People's Democratic Republic 98.332999 98.332999 \n",
"30 Liberia 22.411000 0.000000 \n",
"31 Madagascar 863.040000 587.658000 \n",
"32 Malawi 124.154000 99.535000 \n",
"33 Mexico 3683.104002 3530.820002 \n",
"34 Nepal 0.000000 0.000000 \n",
"35 Nicaragua 671.184000 392.053000 \n",
"36 Nigeria 3.582000 0.922000 \n",
"37 Panama 131.654000 124.200000 \n",
"38 Papua New Guinea 1050.719000 788.353000 \n",
"39 Paraguay 262.502000 120.084000 \n",
"40 Peru 1105.078000 1041.867000 \n",
"41 Philippines 168.282001 103.025000 \n",
"42 Rwanda 779.150000 473.980000 \n",
"43 Sierra Leone 139.744000 103.821000 \n",
"44 Sri Lanka 19.137000 22.168000 \n",
"45 Tanzania 1019.052000 871.329999 \n",
"46 Thailand 1001.435000 540.937000 \n",
"47 Timor-Leste 0.000000 0.000000 \n",
"48 Togo 238.838000 154.834000 \n",
"49 Trinidad & Tobago 30.232000 7.671998 \n",
"50 Uganda 2352.680000 2095.093000 \n",
"51 Venezuela 277.795000 127.889000 \n",
"52 Viet Nam 1145.234001 1200.952000 \n",
"53 Yemen 50.000000 71.666997 \n",
"54 Zambia 24.360000 29.198000 \n",
"55 Zimbabwe 250.772000 239.324000 \n",
"\n",
" 1992 1993 1994 1995 1996 \\\n",
"0 80.250000 38.878000 8.302000 40.559000 51.831000 \n",
"1 0.000000 1.805000 0.050000 0.000000 0.000000 \n",
"2 96.204000 47.319000 84.321000 93.958000 123.445000 \n",
"3 18790.719202 17837.747999 17273.147600 14468.432201 15250.609002 \n",
"4 645.858000 417.609000 507.803000 528.202000 224.076000 \n",
"5 1645.851000 704.530000 545.889000 407.269000 563.549000 \n",
"6 99.975000 137.197000 136.676000 231.542000 98.328000 \n",
"7 16564.370001 13568.362004 11768.089000 9814.197000 10588.430998 \n",
"8 0.336000 0.504000 1.017000 19.149000 12.634000 \n",
"9 2431.340001 2363.920000 2104.968000 2067.011000 2430.134000 \n",
"10 4546.368000 4033.107001 2443.993000 2493.946000 2750.602999 \n",
"11 162.710000 116.195000 135.725000 122.287000 112.171000 \n",
"12 954.229000 919.506000 761.010000 1051.390000 881.561000 \n",
"13 369.467001 331.432000 332.313000 451.018000 452.704000 \n",
"14 1273.435001 1598.666001 2145.251000 1539.973999 1539.411000 \n",
"15 2120.092000 2947.071000 2092.955000 1807.498000 2314.147000 \n",
"16 2.766000 2.463000 0.849000 3.385000 1.659000 \n",
"17 734.461000 1166.778000 1475.325000 1276.118000 1838.231000 \n",
"18 1.007000 2.178000 5.039000 2.946000 1.681000 \n",
"19 32.245000 47.482000 10.815000 38.152000 4.359000 \n",
"20 3327.988000 3750.687000 3274.069000 3700.872001 3978.998000 \n",
"21 57.520000 15.469000 50.467000 151.552000 32.430000 \n",
"22 0.538998 0.052999 0.402997 0.025999 0.337558 \n",
"23 165.976000 158.542000 103.468000 151.788000 156.468000 \n",
"24 1960.071000 1705.317000 1718.185000 1795.963000 2059.760000 \n",
"25 1816.341000 2101.593000 2496.085000 2467.257001 3103.176000 \n",
"26 4603.696000 5662.094001 4604.629999 3946.585001 6440.280000 \n",
"27 22.427000 24.354000 15.826999 26.323000 27.285000 \n",
"28 1384.291000 1438.799000 1358.411000 1450.046000 1901.584000 \n",
"29 101.850000 141.667000 153.000000 165.232999 144.166999 \n",
"30 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"31 736.406000 627.783000 435.621000 585.188000 718.049000 \n",
"32 128.022000 103.574000 78.469000 91.633000 60.151000 \n",
"33 3332.041000 3263.616001 2789.046000 3626.491999 4633.497001 \n",
"34 0.000000 0.008317 0.000000 0.250000 0.083333 \n",
"35 635.946000 476.466000 623.514000 681.178999 822.356000 \n",
"36 11.391000 10.304000 7.873000 15.191000 6.924000 \n",
"37 125.404000 129.286000 76.486000 180.533000 138.598000 \n",
"38 917.174000 1054.634000 1156.837000 1002.052000 1090.076000 \n",
"39 40.369001 58.442000 54.986000 14.006999 14.794000 \n",
"40 1061.043000 775.000000 1057.017000 1760.467000 1679.374000 \n",
"41 41.316000 34.758000 125.669999 57.324001 27.032000 \n",
"42 644.525000 480.725000 30.024000 313.899000 265.306000 \n",
"43 47.020000 40.986000 61.924000 71.475000 28.512000 \n",
"44 14.724000 14.464000 47.359000 21.896000 11.536000 \n",
"45 827.786000 1037.295999 632.137999 744.652999 969.637000 \n",
"46 1163.776001 981.782000 1136.046000 1209.523000 872.686000 \n",
"47 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"48 313.177000 218.284000 165.795000 200.316000 85.490000 \n",
"49 2.451000 9.744000 5.399998 4.059998 3.756998 \n",
"50 1961.034000 1829.081000 3368.765000 3079.261000 4654.503000 \n",
"51 135.818001 525.002000 385.996000 96.752000 494.567000 \n",
"52 1937.611000 2071.837000 2720.911000 3546.405000 3779.415000 \n",
"53 36.989002 50.853002 92.840004 76.874003 42.007002 \n",
"54 30.514000 23.072000 23.954000 20.742000 35.824000 \n",
"55 162.318000 69.828000 31.744000 156.975000 150.362000 \n",
"\n",
" 1997 1998 ... 2009 2010 2011 \\\n",
"0 50.494000 53.929000 ... 6.925000 4.370000 7.575000 \n",
"1 0.000000 0.000000 ... 0.000000 0.000000 0.000000 \n",
"2 110.955000 97.039000 ... 82.608773 78.268006 74.308883 \n",
"3 16801.260005 18144.388334 ... 30377.981636 33166.641590 33806.009328 \n",
"4 528.764000 373.841000 ... 288.830000 307.118958 217.845799 \n",
"5 1368.030000 745.718000 ... 617.757033 793.845667 490.283067 \n",
"6 202.778000 102.320000 ... 61.582000 95.194000 77.943000 \n",
"7 10918.863002 11259.928999 ... 7893.926795 7821.634504 7733.625254 \n",
"8 2.670000 0.000000 ... 0.000000 0.000000 0.000000 \n",
"9 2099.239000 2044.558001 ... 1235.645154 1199.982617 1243.059935 \n",
"10 4712.982001 4365.288002 ... 1806.525716 1912.053443 772.242167 \n",
"11 106.663000 151.941000 ... 7.462000 5.260000 9.755000 \n",
"12 544.253000 640.369000 ... 161.001733 162.109600 131.665067 \n",
"13 306.977000 359.505001 ... 102.396398 41.130774 88.597893 \n",
"14 1044.980999 1056.416000 ... 1086.217400 1201.552727 1532.256606 \n",
"15 2772.126000 1684.247001 ... 1309.200353 1081.766626 1826.306850 \n",
"16 1.120000 1.669000 ... 0.000000 0.000000 0.000000 \n",
"17 1979.733000 1917.061000 ... 1851.497453 3324.102551 2675.419013 \n",
"18 0.000000 1.765000 ... 0.675000 0.810000 0.752333 \n",
"19 29.669000 17.787000 ... 23.675383 6.621935 72.072736 \n",
"20 4243.882000 3541.853000 ... 3492.821987 3468.087678 3696.825498 \n",
"21 114.344000 228.180000 ... 438.785601 405.558528 384.523825 \n",
"22 0.323266 0.228302 ... 1.367328 0.802501 0.590279 \n",
"23 100.776000 127.306000 ... 15.915874 8.740319 8.621106 \n",
"24 1722.482000 2329.274000 ... 3084.187828 3349.397639 3947.141151 \n",
"25 2640.111001 3486.830003 ... 3006.562463 4647.281111 5414.010577 \n",
"26 5755.078000 5598.031999 ... 7907.273516 5489.147556 3919.891883 \n",
"27 29.682000 16.981000 ... 24.830517 15.434930 16.059855 \n",
"28 1159.095000 841.489000 ... 524.748000 531.092657 609.163192 \n",
"29 110.083000 111.688000 ... 263.861776 287.857831 420.965895 \n",
"30 0.000000 3.244000 ... 9.120000 7.216000 7.879316 \n",
"31 480.233000 796.116000 ... 39.637137 73.584903 143.910720 \n",
"32 60.170000 58.600000 ... 17.575000 8.119890 25.853283 \n",
"33 4502.424001 3399.026001 ... 2838.096450 2497.539985 2906.517574 \n",
"34 0.066667 0.033333 ... 1.508308 1.036625 3.748284 \n",
"35 714.003000 940.583999 ... 1373.999130 1711.816058 1467.709799 \n",
"36 6.905000 9.897000 ... 0.642488 3.044153 6.553999 \n",
"37 124.107000 145.123000 ... 58.994871 65.335326 53.088844 \n",
"38 1043.437000 1348.968000 ... 1026.972830 929.341207 1224.522669 \n",
"39 9.703000 41.266000 ... 0.018460 0.195814 0.019942 \n",
"40 1647.733000 1949.254000 ... 3073.578000 3816.671040 4697.069665 \n",
"41 30.928999 21.003000 ... 6.731293 6.366546 10.326075 \n",
"42 217.949000 214.272000 ... 304.316256 295.962933 276.811683 \n",
"43 46.943000 41.865000 ... 103.733457 57.576486 37.387028 \n",
"44 21.136000 27.266000 ... 0.649418 1.655244 2.354069 \n",
"45 655.166999 742.305000 ... 1156.944317 555.801933 798.099933 \n",
"46 1066.230000 778.031000 ... 185.387559 369.627534 242.753954 \n",
"47 0.000000 0.000000 ... 43.197228 68.039268 38.721169 \n",
"48 313.137000 165.801000 ... 151.703783 198.107700 155.084167 \n",
"49 3.951999 5.218999 ... 0.143219 0.500076 0.299840 \n",
"50 3501.942000 3286.012000 ... 3014.351000 2656.536000 3142.011000 \n",
"51 104.537000 267.563000 ... 18.181522 19.084311 0.467082 \n",
"52 6177.834000 6466.712000 ... 17051.734477 14228.585796 17717.394731 \n",
"53 79.789003 70.790999 ... 39.363849 43.530425 35.276688 \n",
"54 38.890000 33.308000 ... 32.547667 17.871333 9.003000 \n",
"55 109.008000 153.268000 ... 18.478566 7.434416 4.562206 \n",
"\n",
" 2012 2013 2014 2015 2016 \\\n",
"0 8.375000 5.520000 9.375000 10.515000 10.945000 \n",
"1 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"2 62.675780 54.850533 61.751267 30.280158 22.456342 \n",
"3 28549.425891 31650.562945 37335.172825 37562.846747 34269.150253 \n",
"4 392.006917 194.715883 252.178000 230.188550 261.295433 \n",
"5 621.812800 271.949217 375.033867 390.142717 281.128967 \n",
"6 77.692000 1.000000 75.027000 43.214000 80.018000 \n",
"7 7170.203291 9669.907367 10954.408357 12716.384670 12831.390727 \n",
"8 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"9 1373.667164 1343.951587 1208.918498 1128.190321 1006.928354 \n",
"10 1711.765617 1962.070767 1489.151150 1418.375167 1432.043600 \n",
"11 15.312000 12.415000 13.241000 12.796050 15.402100 \n",
"12 146.383933 140.125333 150.577000 128.285000 170.955333 \n",
"13 137.383017 64.723312 44.834272 19.143967 19.362536 \n",
"14 1579.590942 1262.125910 1129.165184 869.048494 922.626339 \n",
"15 1043.986010 1102.743425 461.649110 575.864057 495.993015 \n",
"16 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"17 3202.592407 2870.075248 3116.690370 2984.974817 3000.724450 \n",
"18 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"19 30.467063 22.610512 26.441978 40.021570 26.801713 \n",
"20 3750.112536 3575.011397 3043.438535 2960.862008 2991.120758 \n",
"21 377.203544 134.277132 24.120806 151.692689 250.029166 \n",
"22 1.767020 1.110391 12.539913 1.976087 1.967133 \n",
"23 11.061672 8.279471 3.141473 2.661146 1.277018 \n",
"24 5507.984939 4185.118924 4251.826667 5029.667006 5305.953308 \n",
"25 5043.961929 5032.638529 5130.914270 5262.293887 6086.119737 \n",
"26 8205.740826 9254.807331 6174.809713 8378.687596 6545.392045 \n",
"27 14.919451 15.053724 10.248693 11.873981 11.223999 \n",
"28 802.886282 814.542248 798.726789 712.056129 727.043152 \n",
"29 359.761897 399.651110 369.726347 403.169902 385.779741 \n",
"30 2.810967 3.539333 1.345667 3.520000 2.955000 \n",
"31 78.917471 166.138449 137.809592 59.325518 53.302978 \n",
"32 19.529999 28.729707 25.535307 17.859220 22.148678 \n",
"33 3556.455805 3132.049713 2479.526809 2457.996501 2232.913073 \n",
"34 1.428570 1.220352 0.862922 2.024575 2.071278 \n",
"35 1987.302147 1660.549988 1900.637384 1753.158784 1963.467307 \n",
"36 1.662009 0.721269 1.432306 2.862852 1.400562 \n",
"37 50.592833 48.549188 51.024794 49.714221 55.585032 \n",
"38 924.930099 810.932640 806.833940 711.482100 1133.180307 \n",
"39 0.006549 0.009527 0.072768 0.000000 0.001983 \n",
"40 4310.351922 3736.075980 2719.889850 2789.839140 3959.583593 \n",
"41 3.090991 6.092139 12.037538 14.831043 12.943474 \n",
"42 250.844586 254.266747 255.682678 262.683550 244.335462 \n",
"43 65.270074 56.991638 24.031958 54.039745 25.806877 \n",
"44 0.152959 0.280154 2.338358 0.482531 1.449361 \n",
"45 756.221317 934.794780 717.956937 708.912463 904.797654 \n",
"46 349.719366 49.307703 27.370462 578.329072 130.952572 \n",
"47 55.877050 70.239917 111.062363 67.686005 67.853308 \n",
"48 147.390447 90.756533 133.968633 178.104817 74.876833 \n",
"49 1.448238 2.485916 2.137128 1.932274 1.764547 \n",
"50 2685.237000 3671.879000 3442.351000 3595.607000 3543.097000 \n",
"51 2.314776 5.763470 0.082086 0.503640 0.000000 \n",
"52 22919.663832 19717.764414 26097.144160 20654.510096 27568.141684 \n",
"53 62.651596 55.349134 56.989046 52.232413 37.901260 \n",
"54 7.075727 10.254890 5.244187 2.939550 9.791119 \n",
"55 4.003538 2.180804 11.302193 11.069010 12.287078 \n",
"\n",
" 2017 2018 \n",
"0 9.055000 9.323397 \n",
"1 0.000000 0.000000 \n",
"2 26.119992 22.459634 \n",
"3 30924.567849 35382.556487 \n",
"4 168.876264 201.725236 \n",
"5 245.017117 287.415250 \n",
"6 18.112667 38.528000 \n",
"7 12984.595747 12807.972625 \n",
"8 0.000000 0.000000 \n",
"9 987.154641 1207.946296 \n",
"10 854.620583 1522.457567 \n",
"11 15.126967 33.346885 \n",
"12 88.618991 187.999667 \n",
"13 24.396048 32.203177 \n",
"14 695.146297 482.699333 \n",
"15 527.101081 571.424066 \n",
"16 0.000000 0.058001 \n",
"17 3773.406333 3589.048020 \n",
"18 0.000000 0.016813 \n",
"19 16.855640 12.400351 \n",
"20 3382.666191 3327.489656 \n",
"21 215.691471 156.505008 \n",
"22 0.811538 1.178945 \n",
"23 1.886211 2.019124 \n",
"24 7340.843952 7143.884471 \n",
"25 6541.501049 5967.224604 \n",
"26 8197.573974 4538.839055 \n",
"27 10.232406 9.751569 \n",
"28 709.638235 753.030915 \n",
"29 385.587653 361.021635 \n",
"30 11.011667 7.733333 \n",
"31 63.663211 6.475766 \n",
"32 12.622395 12.079999 \n",
"33 2910.649964 2888.140482 \n",
"34 0.974511 1.845253 \n",
"35 2448.637930 2300.035032 \n",
"36 2.579840 3.012917 \n",
"37 50.141141 46.893515 \n",
"38 794.100395 869.945983 \n",
"39 0.010452 0.013536 \n",
"40 3945.823228 4063.938661 \n",
"41 8.548641 3.992724 \n",
"42 245.941270 265.649490 \n",
"43 34.973872 26.546347 \n",
"44 1.689305 1.537915 \n",
"45 664.079320 856.004136 \n",
"46 141.126331 258.133657 \n",
"47 86.277889 128.668467 \n",
"48 27.328767 111.044350 \n",
"49 1.800000 2.870569 \n",
"50 4774.033000 4223.182000 \n",
"51 0.000000 71.772996 \n",
"52 22439.364593 27866.141588 \n",
"53 46.422101 42.118131 \n",
"54 12.568981 26.168988 \n",
"55 2.349193 4.805310 \n",
"\n",
"[56 rows x 30 columns]"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[1]"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "stupid-migration",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:10:47.853190Z",
"iopub.status.busy": "2021-04-04T17:10:47.852412Z",
"iopub.status.idle": "2021-04-04T17:10:47.856343Z",
"shell.execute_reply": "2021-04-04T17:10:47.855685Z"
},
"papermill": {
"duration": 0.021624,
"end_time": "2021-04-04T17:10:47.856507",
"exception": false,
"start_time": "2021-04-04T17:10:47.834883",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def get_means(df):\n",
" df=df.copy()\n",
" countries=df[df.columns[0]]\n",
" mean=df.mean(axis=1)\n",
" df=pd.concat([countries,mean],axis=1)\n",
" df.columns=['country',countries.name]\n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "blocked-bulgarian",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:10:47.886053Z",
"iopub.status.busy": "2021-04-04T17:10:47.885038Z",
"iopub.status.idle": "2021-04-04T17:10:47.888444Z",
"shell.execute_reply": "2021-04-04T17:10:47.889017Z"
},
"papermill": {
"duration": 0.021029,
"end_time": "2021-04-04T17:10:47.889250",
"exception": false,
"start_time": "2021-04-04T17:10:47.868221",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": [
"def make_df(dfs):\n",
" #Processs all Dataframme into one Dataframe\n",
" process_dfs=[]\n",
" \n",
" for df in dfs:\n",
" processed_dfs.append(get_means(df))"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "preliminary-insured",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:10:47.921624Z",
"iopub.status.busy": "2021-04-04T17:10:47.918579Z",
"iopub.status.idle": "2021-04-04T17:10:47.955156Z",
"shell.execute_reply": "2021-04-04T17:10:47.954422Z"
},
"papermill": {
"duration": 0.054327,
"end_time": "2021-04-04T17:10:47.955317",
"exception": false,
"start_time": "2021-04-04T17:10:47.900990",
"status": "completed"
},
"tags": []
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>domestic_consumption</th>\n",
" <th>exports</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Angola</td>\n",
" <td>25.689655</td>\n",
" <td>24.115531</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Bolivia (Plurinational State of)</td>\n",
" <td>41.103448</td>\n",
" <td>78.384152</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Brazil</td>\n",
" <td>15234.310345</td>\n",
" <td>25706.195606</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>Burundi</td>\n",
" <td>1.891966</td>\n",
" <td>363.186423</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Ecuador</td>\n",
" <td>214.137931</td>\n",
" <td>1115.800914</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Indonesia</td>\n",
" <td>2662.137931</td>\n",
" <td>5878.047357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>Madagascar</td>\n",
" <td>325.405724</td>\n",
" <td>289.048949</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Malawi</td>\n",
" <td>1.310345</td>\n",
" <td>47.247465</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Papua New Guinea</td>\n",
" <td>2.004828</td>\n",
" <td>1015.455512</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Paraguay</td>\n",
" <td>19.482759</td>\n",
" <td>25.757542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Peru</td>\n",
" <td>222.413793</td>\n",
" <td>2688.290420</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>Rwanda</td>\n",
" <td>1.195379</td>\n",
" <td>316.250499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Timor-Leste</td>\n",
" <td>0.152069</td>\n",
" <td>35.083247</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>Zimbabwe</td>\n",
" <td>4.802276</td>\n",
" <td>78.112883</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>Congo</td>\n",
" <td>2.977034</td>\n",
" <td>1.341862</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>Cuba</td>\n",
" <td>213.244828</td>\n",
" <td>63.499287</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>Dominican Republic</td>\n",
" <td>356.438724</td>\n",
" <td>180.744993</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>Haiti</td>\n",
" <td>333.275862</td>\n",
" <td>62.019893</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>Philippines</td>\n",
" <td>1501.310345</td>\n",
" <td>29.944000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Tanzania</td>\n",
" <td>40.646586</td>\n",
" <td>786.307663</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>Zambia</td>\n",
" <td>0.570069</td>\n",
" <td>42.855127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Benin</td>\n",
" <td>0.000000</td>\n",
" <td>0.063966</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>Cameroon</td>\n",
" <td>79.787897</td>\n",
" <td>795.942577</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Central African Republic</td>\n",
" <td>13.560000</td>\n",
" <td>97.366456</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>Colombia</td>\n",
" <td>1388.097669</td>\n",
" <td>10953.061020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>Costa Rica</td>\n",
" <td>370.238621</td>\n",
" <td>1683.603825</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Côte d'Ivoire</td>\n",
" <td>224.850655</td>\n",
" <td>2640.989406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>Democratic Republic of Congo</td>\n",
" <td>200.000000</td>\n",
" <td>438.641994</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>El Salvador</td>\n",
" <td>229.725310</td>\n",
" <td>1542.671788</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>Equatorial Guinea</td>\n",
" <td>0.000000</td>\n",
" <td>0.881828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>Ethiopia</td>\n",
" <td>2529.034483</td>\n",
" <td>2257.551574</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>Gabon</td>\n",
" <td>0.628241</td>\n",
" <td>1.060177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>Ghana</td>\n",
" <td>5.661379</td>\n",
" <td>30.461676</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>Guatemala</td>\n",
" <td>326.034483</td>\n",
" <td>3590.512097</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>Guinea</td>\n",
" <td>48.120690</td>\n",
" <td>225.830006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>Guyana</td>\n",
" <td>4.978759</td>\n",
" <td>1.305947</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>Honduras</td>\n",
" <td>259.109586</td>\n",
" <td>3194.853765</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>India</td>\n",
" <td>1153.137931</td>\n",
" <td>3777.195530</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>Jamaica</td>\n",
" <td>10.429897</td>\n",
" <td>19.966535</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>Kenya</td>\n",
" <td>52.241379</td>\n",
" <td>989.200405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>Lao People's Democratic Republic</td>\n",
" <td>85.379310</td>\n",
" <td>243.108008</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>Liberia</td>\n",
" <td>4.793103</td>\n",
" <td>4.361228</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>Mexico</td>\n",
" <td>1749.517241</td>\n",
" <td>3143.855086</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>Nepal</td>\n",
" <td>0.000000</td>\n",
" <td>0.903943</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>Nicaragua</td>\n",
" <td>165.000172</td>\n",
" <td>1270.768988</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>Nigeria</td>\n",
" <td>39.275862</td>\n",
" <td>6.434295</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>Panama</td>\n",
" <td>68.331586</td>\n",
" <td>91.047137</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>Sierra Leone</td>\n",
" <td>5.620690</td>\n",
" <td>50.098006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>Sri Lanka</td>\n",
" <td>32.298862</td>\n",
" <td>9.234620</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>Thailand</td>\n",
" <td>669.310345</td>\n",
" <td>551.847711</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td>Togo</td>\n",
" <td>1.236793</td>\n",
" <td>165.468715</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>Trinidad & Tobago</td>\n",
" <td>11.775862</td>\n",
" <td>3.671784</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>Uganda</td>\n",
" <td>154.928966</td>\n",
" <td>3080.789345</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>Venezuela</td>\n",
" <td>1327.337931</td>\n",
" <td>136.791798</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>Viet Nam</td>\n",
" <td>1016.050759</td>\n",
" <td>13048.102878</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>Yemen</td>\n",
" <td>67.689655</td>\n",
" <td>57.718630</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country domestic_consumption exports\n",
"0 Angola 25.689655 24.115531\n",
"1 Bolivia (Plurinational State of) 41.103448 78.384152\n",
"2 Brazil 15234.310345 25706.195606\n",
"3 Burundi 1.891966 363.186423\n",
"4 Ecuador 214.137931 1115.800914\n",
"5 Indonesia 2662.137931 5878.047357\n",
"6 Madagascar 325.405724 289.048949\n",
"7 Malawi 1.310345 47.247465\n",
"8 Papua New Guinea 2.004828 1015.455512\n",
"9 Paraguay 19.482759 25.757542\n",
"10 Peru 222.413793 2688.290420\n",
"11 Rwanda 1.195379 316.250499\n",
"12 Timor-Leste 0.152069 35.083247\n",
"13 Zimbabwe 4.802276 78.112883\n",
"14 Congo 2.977034 1.341862\n",
"15 Cuba 213.244828 63.499287\n",
"16 Dominican Republic 356.438724 180.744993\n",
"17 Haiti 333.275862 62.019893\n",
"18 Philippines 1501.310345 29.944000\n",
"19 Tanzania 40.646586 786.307663\n",
"20 Zambia 0.570069 42.855127\n",
"21 Benin 0.000000 0.063966\n",
"22 Cameroon 79.787897 795.942577\n",
"23 Central African Republic 13.560000 97.366456\n",
"24 Colombia 1388.097669 10953.061020\n",
"25 Costa Rica 370.238621 1683.603825\n",
"26 Côte d'Ivoire 224.850655 2640.989406\n",
"27 Democratic Republic of Congo 200.000000 438.641994\n",
"28 El Salvador 229.725310 1542.671788\n",
"29 Equatorial Guinea 0.000000 0.881828\n",
"30 Ethiopia 2529.034483 2257.551574\n",
"31 Gabon 0.628241 1.060177\n",
"32 Ghana 5.661379 30.461676\n",
"33 Guatemala 326.034483 3590.512097\n",
"34 Guinea 48.120690 225.830006\n",
"35 Guyana 4.978759 1.305947\n",
"36 Honduras 259.109586 3194.853765\n",
"37 India 1153.137931 3777.195530\n",
"38 Jamaica 10.429897 19.966535\n",
"39 Kenya 52.241379 989.200405\n",
"40 Lao People's Democratic Republic 85.379310 243.108008\n",
"41 Liberia 4.793103 4.361228\n",
"42 Mexico 1749.517241 3143.855086\n",
"43 Nepal 0.000000 0.903943\n",
"44 Nicaragua 165.000172 1270.768988\n",
"45 Nigeria 39.275862 6.434295\n",
"46 Panama 68.331586 91.047137\n",
"47 Sierra Leone 5.620690 50.098006\n",
"48 Sri Lanka 32.298862 9.234620\n",
"49 Thailand 669.310345 551.847711\n",
"50 Togo 1.236793 165.468715\n",
"51 Trinidad & Tobago 11.775862 3.671784\n",
"52 Uganda 154.928966 3080.789345\n",
"53 Venezuela 1327.337931 136.791798\n",
"54 Viet Nam 1016.050759 13048.102878\n",
"55 Yemen 67.689655 57.718630"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_means(df[0]).merge(get_means(df[1]),on='country')"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "bibliographic-steering",
"metadata": {
"execution": {
"iopub.execute_input": "2021-04-04T17:10:47.989744Z",
"iopub.status.busy": "2021-04-04T17:10:47.988878Z",
"iopub.status.idle": "2021-04-04T17:10:47.994272Z",
"shell.execute_reply": "2021-04-04T17:10:47.993617Z"
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},
"tags": []
},
"outputs": [
{
"data": {
"text/plain": [
"0 25.689655\n",
"1 41.103448\n",
"2 15234.310345\n",
"3 1.891966\n",
"4 214.137931\n",
"5 2662.137931\n",
"6 325.405724\n",
"7 1.310345\n",
"8 2.004828\n",
"9 19.482759\n",
"10 222.413793\n",
"11 1.195379\n",
"12 0.152069\n",
"13 4.802276\n",
"14 2.977034\n",
"15 213.244828\n",
"16 356.438724\n",
"17 333.275862\n",
"18 1501.310345\n",
"19 40.646586\n",
"20 0.570069\n",
"21 0.000000\n",
"22 79.787897\n",
"23 13.560000\n",
"24 1388.097669\n",
"25 370.238621\n",
"26 224.850655\n",
"27 200.000000\n",
"28 229.725310\n",
"29 0.000000\n",
"30 2529.034483\n",
"31 0.628241\n",
"32 5.661379\n",
"33 326.034483\n",
"34 48.120690\n",
"35 4.978759\n",
"36 259.109586\n",
"37 1153.137931\n",
"38 10.429897\n",
"39 52.241379\n",
"40 85.379310\n",
"41 4.793103\n",
"42 1749.517241\n",
"43 0.000000\n",
"44 165.000172\n",
"45 39.275862\n",
"46 68.331586\n",
"47 5.620690\n",
"48 32.298862\n",
"49 669.310345\n",
"50 1.236793\n",
"51 11.775862\n",
"52 154.928966\n",
"53 1327.337931\n",
"54 1016.050759\n",
"55 67.689655\n",
"dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[0].mean(axis=1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "exempt-success",
"metadata": {
"papermill": {
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"end_time": "2021-04-04T17:10:48.019730",
"exception": false,
"start_time": "2021-04-04T17:10:48.007269",
"status": "completed"
},
"tags": []
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.9"
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"papermill": {
"default_parameters": {},
"duration": 8.101857,
"end_time": "2021-04-04T17:10:48.846418",
"environment_variables": {},
"exception": null,
"input_path": "__notebook__.ipynb",
"output_path": "__notebook__.ipynb",
"parameters": {},
"start_time": "2021-04-04T17:10:40.744561",
"version": "2.3.2"
}
},
"nbformat": 4,
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| 0058/724/58724515.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
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"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0058/724/58724746.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0058/724/58724903.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"markdown\",\n \"id\": \"surprised-cameroon\",\n \"me(...TRUNCATED) | 0058/725/58725036.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0058/725/58725132.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"id\": \"certif(...TRUNCATED) | 0058/725/58725488.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
"{\"metadata\":{\"kernelspec\":{\"language\":\"python\",\"display_name\":\"Python 3\",\"name\":\"pyt(...TRUNCATED) | 0058/725/58725834.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
"{\n \"cells\": [\n {\n \"cell_type\": \"code\",\n \"execution_count\": 1,\n \"metadata\": {\(...TRUNCATED) | 0058/725/58725971.ipynb | s3://data-agents/kaggle-outputs/sharded/020_00058.jsonl.gz |
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