{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## DataFrame Creation\n",
"\n",
"In this notebook, we will learn to create new ```DataFrame``` object from other data structures( e.g.,numpy array and dictionary) and convert data frame to numpy array and dictionary. The defult setting for pandas ```DataFrame``` is \n",
"\n",
"```pandas.DataFrame(data=None, index=None, columns=None, dtype=None, copy=False)```"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"sns.set()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 1. To create new ```DataFrame``` from Numpy array. \n",
"\n",
"Let's create a random array of size(100,20) and random column names. We will use these array and column names to create the ```DataFrame``` in next step."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import random as random\n",
"A = np.random.rand(100,10)\n",
"letter = ['A','B','C','D','E','F','G','H','X']\n",
"\n",
"def namer(n):\n",
" col_names = [ random.choice(letter)\\\n",
" +random.choice(letter)\\\n",
" +random.choice(letter)\\\n",
" +random.choice(letter) for i in range(n)]\n",
" return col_names"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['HHEE', 'FEGD', 'BFHC', 'HXFC', 'CBDF', 'DEDH', 'CBCX', 'XGXB', 'GCBC', 'FDEE']\n"
]
}
],
"source": [
"print(namer(A.shape[1]))"
]
},
{
"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>AAGF</th>\n",
" <th>XBGA</th>\n",
" <th>DXAC</th>\n",
" <th>XEDB</th>\n",
" <th>EDCG</th>\n",
" <th>ABDH</th>\n",
" <th>GFHX</th>\n",
" <th>FAAB</th>\n",
" <th>BBEC</th>\n",
" <th>DGDC</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.285058</td>\n",
" <td>0.456733</td>\n",
" <td>0.841292</td>\n",
" <td>0.397957</td>\n",
" <td>0.888982</td>\n",
" <td>0.970782</td>\n",
" <td>0.379687</td>\n",
" <td>0.565177</td>\n",
" <td>0.657939</td>\n",
" <td>0.461385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.725005</td>\n",
" <td>0.432756</td>\n",
" <td>0.037801</td>\n",
" <td>0.020203</td>\n",
" <td>0.590901</td>\n",
" <td>0.590571</td>\n",
" <td>0.623529</td>\n",
" <td>0.166581</td>\n",
" <td>0.179392</td>\n",
" <td>0.454290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.668743</td>\n",
" <td>0.352332</td>\n",
" <td>0.642905</td>\n",
" <td>0.427461</td>\n",
" <td>0.025124</td>\n",
" <td>0.365414</td>\n",
" <td>0.609983</td>\n",
" <td>0.568686</td>\n",
" <td>0.522738</td>\n",
" <td>0.525048</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.807165</td>\n",
" <td>0.827478</td>\n",
" <td>0.542088</td>\n",
" <td>0.628743</td>\n",
" <td>0.616745</td>\n",
" <td>0.386370</td>\n",
" <td>0.632225</td>\n",
" <td>0.387794</td>\n",
" <td>0.618686</td>\n",
" <td>0.786503</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.645945</td>\n",
" <td>0.136078</td>\n",
" <td>0.769546</td>\n",
" <td>0.721885</td>\n",
" <td>0.107891</td>\n",
" <td>0.128859</td>\n",
" <td>0.938451</td>\n",
" <td>0.875492</td>\n",
" <td>0.647702</td>\n",
" <td>0.148635</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" AAGF XBGA DXAC XEDB EDCG ABDH GFHX \\\n",
"0 0.285058 0.456733 0.841292 0.397957 0.888982 0.970782 0.379687 \n",
"1 0.725005 0.432756 0.037801 0.020203 0.590901 0.590571 0.623529 \n",
"2 0.668743 0.352332 0.642905 0.427461 0.025124 0.365414 0.609983 \n",
"3 0.807165 0.827478 0.542088 0.628743 0.616745 0.386370 0.632225 \n",
"4 0.645945 0.136078 0.769546 0.721885 0.107891 0.128859 0.938451 \n",
"\n",
" FAAB BBEC DGDC \n",
"0 0.565177 0.657939 0.461385 \n",
"1 0.166581 0.179392 0.454290 \n",
"2 0.568686 0.522738 0.525048 \n",
"3 0.387794 0.618686 0.786503 \n",
"4 0.875492 0.647702 0.148635 "
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.DataFrame(A, columns = col_names )\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"- To save data from ```new DataFrame``` to a file:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv('data/test.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 2. To create new ```DataFrame``` from list of dictionaries.\n",
"\n",
"Here we will create a list with collection of dictionaries. Each of the dictionary will have keys and values. Using this list of dictionaries, we will create another ```DataFrame```. The keys of the dictionary will serve as the column names."
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"LD = []\n",
"for i in range(100):\n",
" LD.append({'Player' : namer(1)[0],\\\n",
" 'game1' : random.uniform(0,1),\\\n",
" 'game2' : random.uniform(0,1),\\\n",
" 'game3' : random.uniform(0,1),\n",
" 'game4' : random.uniform(0,1),\n",
" 'game5' : random.uniform(0,1)})"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Player': 'BGXB',\n",
" 'game1': 0.2965944756471328,\n",
" 'game2': 0.11334763879800447,\n",
" 'game3': 0.028543866127768824,\n",
" 'game4': 0.225405432495144,\n",
" 'game5': 0.05423542200055986}"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"LD[0]"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"DF = pd.DataFrame(LD)\n",
"DF=DF.set_index(\"Player\")"
]
},
{
"cell_type": "code",
"execution_count": 21,
"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>game1</th>\n",
" <th>game2</th>\n",
" <th>game3</th>\n",
" <th>game4</th>\n",
" <th>game5</th>\n",
" </tr>\n",
" <tr>\n",
" <th>Player</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>BGXB</th>\n",
" <td>0.296594</td>\n",
" <td>0.113348</td>\n",
" <td>0.028544</td>\n",
" <td>0.225405</td>\n",
" <td>0.054235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>DBDB</th>\n",
" <td>0.047226</td>\n",
" <td>0.107065</td>\n",
" <td>0.801571</td>\n",
" <td>0.816877</td>\n",
" <td>0.556934</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AXXH</th>\n",
" <td>0.862611</td>\n",
" <td>0.439051</td>\n",
" <td>0.083341</td>\n",
" <td>0.389785</td>\n",
" <td>0.258748</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BXED</th>\n",
" <td>0.643533</td>\n",
" <td>0.082176</td>\n",
" <td>0.167241</td>\n",
" <td>0.405304</td>\n",
" <td>0.088063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>FXGE</th>\n",
" <td>0.279076</td>\n",
" <td>0.000998</td>\n",
" <td>0.949414</td>\n",
" <td>0.303408</td>\n",
" <td>0.009342</td>\n",
" </tr>\n",
" <tr>\n",
" <th>AHDC</th>\n",
" <td>0.617194</td>\n",
" <td>0.272401</td>\n",
" <td>0.252663</td>\n",
" <td>0.788798</td>\n",
" <td>0.130996</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CXGA</th>\n",
" <td>0.104552</td>\n",
" <td>0.895106</td>\n",
" <td>0.414877</td>\n",
" <td>0.167643</td>\n",
" <td>0.454175</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BDBA</th>\n",
" <td>0.045650</td>\n",
" <td>0.926742</td>\n",
" <td>0.454097</td>\n",
" <td>0.055006</td>\n",
" <td>0.939082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>HBDC</th>\n",
" <td>0.069192</td>\n",
" <td>0.797224</td>\n",
" <td>0.943648</td>\n",
" <td>0.567334</td>\n",
" <td>0.044285</td>\n",
" </tr>\n",
" <tr>\n",
" <th>EBBX</th>\n",
" <td>0.383365</td>\n",
" <td>0.852788</td>\n",
" <td>0.679330</td>\n",
" <td>0.418570</td>\n",
" <td>0.817291</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" game1 game2 game3 game4 game5\n",
"Player \n",
"BGXB 0.296594 0.113348 0.028544 0.225405 0.054235\n",
"DBDB 0.047226 0.107065 0.801571 0.816877 0.556934\n",
"AXXH 0.862611 0.439051 0.083341 0.389785 0.258748\n",
"BXED 0.643533 0.082176 0.167241 0.405304 0.088063\n",
"FXGE 0.279076 0.000998 0.949414 0.303408 0.009342\n",
"AHDC 0.617194 0.272401 0.252663 0.788798 0.130996\n",
"CXGA 0.104552 0.895106 0.414877 0.167643 0.454175\n",
"BDBA 0.045650 0.926742 0.454097 0.055006 0.939082\n",
"HBDC 0.069192 0.797224 0.943648 0.567334 0.044285\n",
"EBBX 0.383365 0.852788 0.679330 0.418570 0.817291"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"DF.head(10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### 3. To create ```DataFrame``` from a List :"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>A</th>\n",
" <th>B</th>\n",
" <th>C</th>\n",
" <th>D</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.816389</td>\n",
" <td>0.212530</td>\n",
" <td>0.705185</td>\n",
" <td>0.225743</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.646496</td>\n",
" <td>0.876869</td>\n",
" <td>0.648350</td>\n",
" <td>0.788687</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.869173</td>\n",
" <td>0.832004</td>\n",
" <td>0.516009</td>\n",
" <td>0.917783</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>0.668866</td>\n",
" <td>0.778850</td>\n",
" <td>0.834528</td>\n",
" <td>0.243842</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.742311</td>\n",
" <td>0.984313</td>\n",
" <td>0.872512</td>\n",
" <td>0.451476</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" A B C D\n",
"0 0.816389 0.212530 0.705185 0.225743\n",
"1 0.646496 0.876869 0.648350 0.788687\n",
"2 0.869173 0.832004 0.516009 0.917783\n",
"3 0.668866 0.778850 0.834528 0.243842\n",
"4 0.742311 0.984313 0.872512 0.451476"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"A = [random.uniform(0,1)for i in range(10)]\n",
"B = [random.uniform(0,1)for i in range(10)]\n",
"C = [random.uniform(0,1)for i in range(10)]\n",
"D = [random.uniform(0,1)for i in range(10)]\n",
"\n",
"df = pd.DataFrame()\n",
"df['A'],df['B'],df['C'],df['D'] = A,B,C,D\n",
"df.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### References:\n",
"1. [Pydata document for Styling DataFrame visualization](https://pandas.pydata.org/docs/user_guide/style.html)"
]
}
],
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"display_name": "Python 3",
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