Python Programming Assignment Help

Python Task on PandasProblems

{
“cells”: [
{
“cell_type”: “code”,
“execution_count”: 195,
“metadata”: {},
“outputs”: [
{
“name”: “stdout”,
“output_type”: “stream”,
“text”: [
” custID accountID tenure_mo account_type\n”,
“0 1 1 2 BusiNESS\n”,
“1 1 2 3 CONSUMER\n”,
“2 2 1 4 consumer\n”,
“3 2 2 4 BUSINESS\n”,
“4 2 3 5 BuSIness\n”,
“5 3 1 6 CONSUmer\n”,
“6 3 2 6 consumer\n”,
“7 4 1 6 CONSUMER\n”,
“8 4 2 7 BUSINESS\n”,
” custID cust_age\n”,
“0 1 20\n”,
“1 2 35\n”,
“2 3 50\n”,
“3 4 85\n”
]
}
],
“source”: [
“#The lines of code below display two real-world data sets: the first contains customer ID and their\n”,
“#corresponding accounts IDs, along with account tenure in months and account type (business or \n”,
“#consumer). The second contains customer IDs and their corresponding age in years.\n”,
“\n”,
“import pandas as pd\n”,
“df1=pd.DataFrame({‘custID’:[1,1,2,2,2,3,3,4,4],\n”,
” ‘accountID’:[1,2,1,2,3,1,2,1,2],\n”,
” ‘tenure_mo’:[2,3,4,4,5,6,6,6,7],\n”,
” ‘account_type’:[‘BusiNESS’,’CONSUMER’,\n”,
” ‘consumer’,\n”,
” ‘BUSINESS’,\n”,
” ‘BuSIness’,\n”,
” ‘CONSUmer’,\n”,
” ‘consumer’,\n”,
” ‘CONSUMER’,\n”,
” ‘BUSINESS’]},columns=[‘custID’,’accountID’,’tenure_mo’,’account_type’])\n”,
“print(df1)\n”,
“df2=pd.DataFrame({‘custID’:[1,2,3,4],\n”,
” ‘cust_age’:[20,35,50,85]},columns=[‘custID’,’cust_age’])\n”,
“print(df2)”
]
},
{
“cell_type”: “code”,
“execution_count”: 4,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: [
“#Question 1: \n”,
“#using pivot tables, write code to display the number of consumer accounts for each customer\n”
]
},
{
“cell_type”: “code”,
“execution_count”: 196,
“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>num_cons_accounts</th>\n”,
” </tr>\n”,
” <tr>\n”,
” <th>custID</th>\n”,
” <th></th>\n”,
” </tr>\n”,
” </thead>\n”,
” <tbody>\n”,
” <tr>\n”,
” <th>1</th>\n”,
” <td>1</td>\n”,
” </tr>\n”,
” <tr>\n”,
” <th>2</th>\n”,
” <td>1</td>\n”,
” </tr>\n”,
” <tr>\n”,
” <th>3</th>\n”,
” <td>2</td>\n”,
” </tr>\n”,
” <tr>\n”,
” <th>4</th>\n”,
” <td>1</td>\n”,
” </tr>\n”,
” </tbody>\n”,
“</table>\n”,
“</div>”
],
“text/plain”: [
” num_cons_accounts\n”,
“custID \n”,
“1 1\n”,
“2 1\n”,
“3 2\n”,
“4 1”
]
},
“execution_count”: 196,
“metadata”: {},
“output_type”: “execute_result”
}
],
“source”: [
“#your code goes here..desired output below\n”,
“df1[‘account_type’] = df1[‘account_type’].apply(lambda x:x.lower())\n”,
“data = pd.pivot_table(df1, columns=[‘account_type’], index=[‘custID’], aggfunc=’count’)\n”,
“data = pd.DataFrame(data[‘accountID’][‘consumer’])\n”,
“data.columns = [‘num_cons_accounts’]\n”,
“data[‘num_cons_accounts’] = data[‘num_cons_accounts’].astype(int)\n”,
“data”
]
},
{
“cell_type”: “code”,
“execution_count”: null,
“metadata”: {
“collapsed”: true
},
“outputs”: [],
“source”: [
“#Question 2: \n”,
“#using group by methods (not pivot table), write code to display a list of only those customer IDs that\n”,
“#have at least 1 business account and that are under 50 years of age.\n”
]
},
{
“cell_type”: “code”,
“execution_count”: 197,
“metadata”: {},
“outputs”: [
{
“data”: {
“text/plain”: [
“[1, 2]”
]
},
“execution_count”: 197,
“metadata”: {},
“output_type”: “execute_result”
}
],
“source”: [
“#your code goes here: \n”,
“#desired output below\n”,
“df1[‘account_type’] = df1[‘account_type’].apply(lambda x:x.lower())\n”,
“data = df1.groupby(‘account_type’)[‘custID’].unique()\n”,
“data = pd.DataFrame(data).reset_index()\n”,
“data = data[data[‘account_type’] == ‘business’]\n”,
“data = pd.DataFrame(data[‘custID’][0].tolist(), columns=[‘custID’])\n”,
“data = data.merge(df2, on=’custID’)\n”,
“data[data[‘cust_age’]<50][‘custID’].tolist()”
]
}
],
“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”: “3.7.3”
}
},
“nbformat”: 4,
“nbformat_minor”: 2
}