Descriptive Analytics:Pasta

Imports

import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import scipy as sp
import seaborn as sns 
import plotnine

sns.set()

Read Data

df = pd.read_csv("W101_PASTAPURCHASE_EDITED.csv"); df.head()

	HHID	TIME	PASTA	EXPOS	AGE	INCOME	AREA
0	1	1	0.939444	1	61.710758	25186.798772	3
1	1	2	2.560969	2	61.710758	25186.798772	3
2	1	3	0.901123	0	61.710758	25186.798772	3
3	1	4	1.916530	1	61.710758	25186.798772	3
4	1	5	1.548751	0	61.710758	25186.798772	3

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 40000 entries, 0 to 39999
Data columns (total 7 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   HHID    40000 non-null  int64  
 1   TIME    40000 non-null  int64  
 2   PASTA   40000 non-null  float64
 3   EXPOS   40000 non-null  int64  
 4   AGE     40000 non-null  float64
 5   INCOME  40000 non-null  float64
 6   AREA    40000 non-null  int64  
dtypes: float64(3), int64(4)
memory usage: 2.1 MB

df.describe()

	HHID	TIME	PASTA	EXPOS	AGE	INCOME	AREA
count	40000.000000	40000.000000	40000.000000	40000.000000	40000.000000	40000.000000	40000.000000
mean	1000.500000	10.500000	1.841545	0.499100	42.251409	27096.126112	2.996000
std	577.357414	5.766353	1.025911	0.702041	13.766646	15928.188291	1.409622
min	1.000000	1.000000	0.000000	0.000000	18.007529	609.107170	1.000000
25%	500.750000	5.750000	1.108936	0.000000	30.505706	15379.591862	2.000000
50%	1000.500000	10.500000	1.811417	0.000000	42.436092	24487.068020	3.000000
75%	1500.250000	15.250000	2.534365	1.000000	54.362395	35272.533787	4.000000
max	2000.000000	20.000000	6.592769	5.000000	64.984126	141066.966883	5.000000

df_agg = df.groupby("AREA")['INCOME'].agg(['min', 'max', 'mean'])

df_agg.sort_values(by='mean')

	min	max	mean
AREA
3	2353.176959	111295.840727	25879.485698
5	1331.260222	141066.966883	26030.910606
2	609.107170	106319.106367	26218.857948
1	2740.776521	85241.329860	28076.599968
4	1241.960729	112983.969631	29260.133137

df.groupby('HHID')['PASTA'].agg(['sum']).sort_values(by='sum', ascending=False)

	sum
HHID
1493	55.361931
1511	51.030304
151	50.816654
647	50.771865
916	50.502666
...	...
92	23.926629
267	23.380097
572	23.225390
1921	21.738494
1917	19.256710

2000 rows × 1 columns

df[(df['AREA']==2) & (df['INCOME']> 20000)].groupby('HHID').agg('sum').query("PASTA>30").count()

TIME      218
PASTA     218
EXPOS     218
AGE       218
INCOME    218
AREA      218
dtype: int64

df[['PASTA', 'EXPOS']].corr()

	PASTA	EXPOS
PASTA	1.000000	0.326617
EXPOS	0.326617	1.000000

df.groupby('HHID')['PASTA'].sum().plot(kind='hist')

<AxesSubplot:ylabel='Frequency'>

df.groupby('TIME')['PASTA'].sum().reset_index().plot.scatter(y='PASTA',x='TIME')

*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2D array with a single row if you intend to specify the same RGB or RGBA value for all points.

<AxesSubplot:xlabel='TIME', ylabel='PASTA'>