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kshirsagarsiddharth/Histogram.ipynb

Created December 15, 2019 10:22
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Histogram.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"df = pd.read_csv('fifadata.csv')"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Unnamed: 0</th>\n",
" <th>ID</th>\n",
" <th>Name</th>\n",
" <th>Age</th>\n",
" <th>Photo</th>\n",
" <th>Nationality</th>\n",
" <th>Flag</th>\n",
" <th>Overall</th>\n",
" <th>Potential</th>\n",
" <th>Club</th>\n",
" <th>...</th>\n",
" <th>Composure</th>\n",
" <th>Marking</th>\n",
" <th>StandingTackle</th>\n",
" <th>SlidingTackle</th>\n",
" <th>GKDiving</th>\n",
" <th>GKHandling</th>\n",
" <th>GKKicking</th>\n",
" <th>GKPositioning</th>\n",
" <th>GKReflexes</th>\n",
" <th>Release Clause</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0</td>\n",
" <td>158023</td>\n",
" <td>L. Messi</td>\n",
" <td>31</td>\n",
" <td>https://cdn.sofifa.org/players/4/19/158023.png</td>\n",
" <td>Argentina</td>\n",
" <td>https://cdn.sofifa.org/flags/52.png</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>FC Barcelona</td>\n",
" <td>...</td>\n",
" <td>96.0</td>\n",
" <td>33.0</td>\n",
" <td>28.0</td>\n",
" <td>26.0</td>\n",
" <td>6.0</td>\n",
" <td>11.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>8.0</td>\n",
" <td>€226.5M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>20801</td>\n",
" <td>Cristiano Ronaldo</td>\n",
" <td>33</td>\n",
" <td>https://cdn.sofifa.org/players/4/19/20801.png</td>\n",
" <td>Portugal</td>\n",
" <td>https://cdn.sofifa.org/flags/38.png</td>\n",
" <td>94</td>\n",
" <td>94</td>\n",
" <td>Juventus</td>\n",
" <td>...</td>\n",
" <td>95.0</td>\n",
" <td>28.0</td>\n",
" <td>31.0</td>\n",
" <td>23.0</td>\n",
" <td>7.0</td>\n",
" <td>11.0</td>\n",
" <td>15.0</td>\n",
" <td>14.0</td>\n",
" <td>11.0</td>\n",
" <td>€127.1M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2</td>\n",
" <td>190871</td>\n",
" <td>Neymar Jr</td>\n",
" <td>26</td>\n",
" <td>https://cdn.sofifa.org/players/4/19/190871.png</td>\n",
" <td>Brazil</td>\n",
" <td>https://cdn.sofifa.org/flags/54.png</td>\n",
" <td>92</td>\n",
" <td>93</td>\n",
" <td>Paris Saint-Germain</td>\n",
" <td>...</td>\n",
" <td>94.0</td>\n",
" <td>27.0</td>\n",
" <td>24.0</td>\n",
" <td>33.0</td>\n",
" <td>9.0</td>\n",
" <td>9.0</td>\n",
" <td>15.0</td>\n",
" <td>15.0</td>\n",
" <td>11.0</td>\n",
" <td>€228.1M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>3</td>\n",
" <td>193080</td>\n",
" <td>De Gea</td>\n",
" <td>27</td>\n",
" <td>https://cdn.sofifa.org/players/4/19/193080.png</td>\n",
" <td>Spain</td>\n",
" <td>https://cdn.sofifa.org/flags/45.png</td>\n",
" <td>91</td>\n",
" <td>93</td>\n",
" <td>Manchester United</td>\n",
" <td>...</td>\n",
" <td>68.0</td>\n",
" <td>15.0</td>\n",
" <td>21.0</td>\n",
" <td>13.0</td>\n",
" <td>90.0</td>\n",
" <td>85.0</td>\n",
" <td>87.0</td>\n",
" <td>88.0</td>\n",
" <td>94.0</td>\n",
" <td>€138.6M</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>4</td>\n",
" <td>192985</td>\n",
" <td>K. De Bruyne</td>\n",
" <td>27</td>\n",
" <td>https://cdn.sofifa.org/players/4/19/192985.png</td>\n",
" <td>Belgium</td>\n",
" <td>https://cdn.sofifa.org/flags/7.png</td>\n",
" <td>91</td>\n",
" <td>92</td>\n",
" <td>Manchester City</td>\n",
" <td>...</td>\n",
" <td>88.0</td>\n",
" <td>68.0</td>\n",
" <td>58.0</td>\n",
" <td>51.0</td>\n",
" <td>15.0</td>\n",
" <td>13.0</td>\n",
" <td>5.0</td>\n",
" <td>10.0</td>\n",
" <td>13.0</td>\n",
" <td>€196.4M</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows × 89 columns</p>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 ID Name Age \\\n",
"0 0 158023 L. Messi 31 \n",
"1 1 20801 Cristiano Ronaldo 33 \n",
"2 2 190871 Neymar Jr 26 \n",
"3 3 193080 De Gea 27 \n",
"4 4 192985 K. De Bruyne 27 \n",
"\n",
" Photo Nationality \\\n",
"0 https://cdn.sofifa.org/players/4/19/158023.png Argentina \n",
"1 https://cdn.sofifa.org/players/4/19/20801.png Portugal \n",
"2 https://cdn.sofifa.org/players/4/19/190871.png Brazil \n",
"3 https://cdn.sofifa.org/players/4/19/193080.png Spain \n",
"4 https://cdn.sofifa.org/players/4/19/192985.png Belgium \n",
"\n",
" Flag Overall Potential \\\n",
"0 https://cdn.sofifa.org/flags/52.png 94 94 \n",
"1 https://cdn.sofifa.org/flags/38.png 94 94 \n",
"2 https://cdn.sofifa.org/flags/54.png 92 93 \n",
"3 https://cdn.sofifa.org/flags/45.png 91 93 \n",
"4 https://cdn.sofifa.org/flags/7.png 91 92 \n",
"\n",
" Club ... Composure Marking StandingTackle SlidingTackle \\\n",
"0 FC Barcelona ... 96.0 33.0 28.0 26.0 \n",
"1 Juventus ... 95.0 28.0 31.0 23.0 \n",
"2 Paris Saint-Germain ... 94.0 27.0 24.0 33.0 \n",
"3 Manchester United ... 68.0 15.0 21.0 13.0 \n",
"4 Manchester City ... 88.0 68.0 58.0 51.0 \n",
"\n",
" GKDiving GKHandling GKKicking GKPositioning GKReflexes Release Clause \n",
"0 6.0 11.0 15.0 14.0 8.0 €226.5M \n",
"1 7.0 11.0 15.0 14.0 11.0 €127.1M \n",
"2 9.0 9.0 15.0 15.0 11.0 €228.1M \n",
"3 90.0 85.0 87.0 88.0 94.0 €138.6M \n",
"4 15.0 13.0 5.0 10.0 13.0 €196.4M \n",
"\n",
"[5 rows x 89 columns]"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x936 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,13))\n",
"plt.hist(df['Age'],color='green',bins=[16,18,22,25,30,34,39,41,45],edgecolor='black')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Frequency')\n",
"plt.title(\"FIFA 2019 Football Players' Age Histogram\")\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"new_fig = plt.figure(figsize=(18,6))\n",
"ax1 = plt.subplot(131)\n",
"ax2 = plt.subplot(132)\n",
"ax3 = plt.subplot(133)\n",
"ax1.hist(df['Age'], color='red',bins=4)\n",
"ax2.hist(df['Age'], color='green',bins=10)\n",
"ax3.hist(df['Age'], color='blue',bins=40);\n",
"ax1.set_title('Histogram with 4 bins')\n",
"ax2.set_title('Histogram with 10 bins')\n",
"ax3.set_title('Histogram with 40 bins')\n",
"ax2.set_xlabel('Age')\n",
"ax1.set_ylabel('Frequency')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1296x1152 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"new_fig = plt.figure(figsize=(18,16))\n",
"for i,bins in enumerate([4,10,40]):\n",
" ax = plt.subplot(4,4,i+1)\n",
" ax.hist(df['Age'],bins=bins,color='blue',edgecolor='black')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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