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hygull/DataScience_Practice.csv

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pandas, data science practice using a simple csv file, use of loc and groupby (little use, you will see more examples in a gist created after this)
Raw
DataScience_Practice.csv
Country City Education Incom Gender
0 United States New York High School 100000 Female
1 United States New York High School 105000 Male
2 United States New York High School 112000 Female
3 United States New York BA 150000 Male
4 United States Las Vegas BA 155000 Female
5 United States Las Vegas BA 160000 Male
6 United States Las Vegas PHD 190000 Female
7 United States Miami PHD 205521 Male
8 United States Miami PHD 210050 Female
9 United Kingdome London High School 100000 Female
10 United Kingdome London High School 105000 Male
11 United Kingdome London High School 112000 Female
12 United Kingdome London BA 150000 Male
13 United Kingdome London BA 155000 Female
14 United Kingdome London BA 160000 Male
15 United Kingdome Manchester PHD 190000 Female
16 United Kingdome Manchester PHD 205521 Male
17 United Kingdome Manchester PHD 210050 Female
18 United Kingdome Manchester High School 100000 Female
19 United Kingdome Liverpol High School 105000 Male
20 United Kingdome Liverpol PHD 112000 Female
21 United Kingdome Liverpol PHD 150000 Male
22 United Kingdome Birmingham PHD 155000 Female
23 United Kingdome Birmingham BA 160000 Male
24 France Paris High School 100000 Female
25 France Paris High School 105000 Male
26 France Paris BA 112000 Female
27 France Paris BA 150000 Male
28 France Nice BA 155000 Female
29 France Nice PHD 160000 Male
30 France Nice PHD 190000 Female
31 France Nice PHD 205521 Male
Display the source blob
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Raw
DataScience_Practice.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 11,
"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>Country</th>\n",
" <th>City</th>\n",
" <th>Education</th>\n",
" <th>Incom</th>\n",
" <th>Gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>100000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>105000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>112000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>BA</td>\n",
" <td>150000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>United States</td>\n",
" <td>Las Vegas</td>\n",
" <td>BA</td>\n",
" <td>155000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>United States</td>\n",
" <td>Las Vegas</td>\n",
" <td>BA</td>\n",
" <td>160000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>United States</td>\n",
" <td>Las Vegas</td>\n",
" <td>PHD</td>\n",
" <td>190000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>United States</td>\n",
" <td>Miami</td>\n",
" <td>PHD</td>\n",
" <td>205521</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>United States</td>\n",
" <td>Miami</td>\n",
" <td>PHD</td>\n",
" <td>210050</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>United Kingdome</td>\n",
" <td>London</td>\n",
" <td>High School</td>\n",
" <td>100000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>United Kingdome</td>\n",
" <td>London</td>\n",
" <td>High School</td>\n",
" <td>105000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>United Kingdome</td>\n",
" <td>London</td>\n",
" <td>High School</td>\n",
" <td>112000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>United Kingdome</td>\n",
" <td>London</td>\n",
" <td>BA</td>\n",
" <td>150000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>United Kingdome</td>\n",
" <td>London</td>\n",
" <td>BA</td>\n",
" <td>155000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>United Kingdome</td>\n",
" <td>London</td>\n",
" <td>BA</td>\n",
" <td>160000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>United Kingdome</td>\n",
" <td>Manchester</td>\n",
" <td>PHD</td>\n",
" <td>190000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>United Kingdome</td>\n",
" <td>Manchester</td>\n",
" <td>PHD</td>\n",
" <td>205521</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>United Kingdome</td>\n",
" <td>Manchester</td>\n",
" <td>PHD</td>\n",
" <td>210050</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>United Kingdome</td>\n",
" <td>Manchester</td>\n",
" <td>High School</td>\n",
" <td>100000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>United Kingdome</td>\n",
" <td>Liverpol</td>\n",
" <td>High School</td>\n",
" <td>105000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>United Kingdome</td>\n",
" <td>Liverpol</td>\n",
" <td>PHD</td>\n",
" <td>112000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>United Kingdome</td>\n",
" <td>Liverpol</td>\n",
" <td>PHD</td>\n",
" <td>150000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>United Kingdome</td>\n",
" <td>Birmingham</td>\n",
" <td>PHD</td>\n",
" <td>155000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>United Kingdome</td>\n",
" <td>Birmingham</td>\n",
" <td>BA</td>\n",
" <td>160000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>France</td>\n",
" <td>Paris</td>\n",
" <td>High School</td>\n",
" <td>100000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>France</td>\n",
" <td>Paris</td>\n",
" <td>High School</td>\n",
" <td>105000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>France</td>\n",
" <td>Paris</td>\n",
" <td>BA</td>\n",
" <td>112000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>France</td>\n",
" <td>Paris</td>\n",
" <td>BA</td>\n",
" <td>150000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>France</td>\n",
" <td>Nice</td>\n",
" <td>BA</td>\n",
" <td>155000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>France</td>\n",
" <td>Nice</td>\n",
" <td>PHD</td>\n",
" <td>160000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>France</td>\n",
" <td>Nice</td>\n",
" <td>PHD</td>\n",
" <td>190000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>France</td>\n",
" <td>Nice</td>\n",
" <td>PHD</td>\n",
" <td>205521</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country City Education Incom Gender\n",
"0 United States New York High School 100000 Female\n",
"1 United States New York High School 105000 Male\n",
"2 United States New York High School 112000 Female\n",
"3 United States New York BA 150000 Male\n",
"4 United States Las Vegas BA 155000 Female\n",
"5 United States Las Vegas BA 160000 Male\n",
"6 United States Las Vegas PHD 190000 Female\n",
"7 United States Miami PHD 205521 Male\n",
"8 United States Miami PHD 210050 Female\n",
"9 United Kingdome London High School 100000 Female\n",
"10 United Kingdome London High School 105000 Male\n",
"11 United Kingdome London High School 112000 Female\n",
"12 United Kingdome London BA 150000 Male\n",
"13 United Kingdome London BA 155000 Female\n",
"14 United Kingdome London BA 160000 Male\n",
"15 United Kingdome Manchester PHD 190000 Female\n",
"16 United Kingdome Manchester PHD 205521 Male\n",
"17 United Kingdome Manchester PHD 210050 Female\n",
"18 United Kingdome Manchester High School 100000 Female\n",
"19 United Kingdome Liverpol High School 105000 Male\n",
"20 United Kingdome Liverpol PHD 112000 Female\n",
"21 United Kingdome Liverpol PHD 150000 Male\n",
"22 United Kingdome Birmingham PHD 155000 Female\n",
"23 United Kingdome Birmingham BA 160000 Male\n",
"24 France Paris High School 100000 Female\n",
"25 France Paris High School 105000 Male\n",
"26 France Paris BA 112000 Female\n",
"27 France Paris BA 150000 Male\n",
"28 France Nice BA 155000 Female\n",
"29 France Nice PHD 160000 Male\n",
"30 France Nice PHD 190000 Female\n",
"31 France Nice PHD 205521 Male"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df = pd.read_excel(\"DataScience_Practice.xlsx\")\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 12,
"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>Country</th>\n",
" <th>City</th>\n",
" <th>Education</th>\n",
" <th>Incom</th>\n",
" <th>Gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>100000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>105000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>112000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>BA</td>\n",
" <td>150000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>United States</td>\n",
" <td>Las Vegas</td>\n",
" <td>BA</td>\n",
" <td>155000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>United States</td>\n",
" <td>Las Vegas</td>\n",
" <td>BA</td>\n",
" <td>160000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>United States</td>\n",
" <td>Las Vegas</td>\n",
" <td>PHD</td>\n",
" <td>190000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>United States</td>\n",
" <td>Miami</td>\n",
" <td>PHD</td>\n",
" <td>205521</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>United States</td>\n",
" <td>Miami</td>\n",
" <td>PHD</td>\n",
" <td>210050</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country City Education Incom Gender\n",
"0 United States New York High School 100000 Female\n",
"1 United States New York High School 105000 Male\n",
"2 United States New York High School 112000 Female\n",
"3 United States New York BA 150000 Male\n",
"4 United States Las Vegas BA 155000 Female\n",
"5 United States Las Vegas BA 160000 Male\n",
"6 United States Las Vegas PHD 190000 Female\n",
"7 United States Miami PHD 205521 Male\n",
"8 United States Miami PHD 210050 Female"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r = (df.groupby('Country').get_group('United States'))\n",
"r\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"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>Country</th>\n",
" <th>City</th>\n",
" <th>Education</th>\n",
" <th>Incom</th>\n",
" <th>Gender</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>100000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>105000</td>\n",
" <td>Male</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>United States</td>\n",
" <td>New York</td>\n",
" <td>High School</td>\n",
" <td>112000</td>\n",
" <td>Female</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Country City Education Incom Gender\n",
"0 United States New York High School 100000 Female\n",
"1 United States New York High School 105000 Male\n",
"2 United States New York High School 112000 Female"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.loc[r[\"Education\"] == \"High School\"]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"317000"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r.loc[r[\"Education\"] == \"High School\"].loc[:, \"Incom\"].sum()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"212000"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"r2 = r.loc[r[\"Education\"] == \"High School\"]\n",
"r2.loc[r2[\"Gender\"] == \"Female\"].loc[:, \"Incom\"].sum()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"317000"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# df.groupby(\"Country\").get(\"United States\")\n",
"\n",
"# Total income of all students of high school who belong to united states\n",
"df.loc[df[\"Country\"] == \"United States\"].loc[df[\"Education\"] == \"High School\"].loc[:, \"Incom\"].sum()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"df.to_csv(\"DataScience_Pratice.csv\")"
]
}
],
"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": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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