Chapter 5 - Basic Math and Statistics
Segment 4 - Summarizing categorical data using pandas
import numpy as np
import pandas as pd
The basics
address = "~/Data/mtcars.csv"
cars = pd.read_csv(address)
cars.columns = ['car_names','mpg','cyl','disp','hp','drat','wt','qsec','vs','am','gear','carb']
cars.index = cars.car_names
cars.head(15)
|
car_names |
mpg |
cyl |
disp |
hp |
drat |
wt |
qsec |
vs |
am |
gear |
carb |
| car_names |
|
|
|
|
|
|
|
|
|
|
|
|
| Mazda RX4 |
Mazda RX4 |
21.0 |
6 |
160.0 |
110 |
3.90 |
2.620 |
16.46 |
0 |
1 |
4 |
4 |
| Mazda RX4 Wag |
Mazda RX4 Wag |
21.0 |
6 |
160.0 |
110 |
3.90 |
2.875 |
17.02 |
0 |
1 |
4 |
4 |
| Datsun 710 |
Datsun 710 |
22.8 |
4 |
108.0 |
93 |
3.85 |
2.320 |
18.61 |
1 |
1 |
4 |
1 |
| Hornet 4 Drive |
Hornet 4 Drive |
21.4 |
6 |
258.0 |
110 |
3.08 |
3.215 |
19.44 |
1 |
0 |
3 |
1 |
| Hornet Sportabout |
Hornet Sportabout |
18.7 |
8 |
360.0 |
175 |
3.15 |
3.440 |
17.02 |
0 |
0 |
3 |
2 |
| Valiant |
Valiant |
18.1 |
6 |
225.0 |
105 |
2.76 |
3.460 |
20.22 |
1 |
0 |
3 |
1 |
| Duster 360 |
Duster 360 |
14.3 |
8 |
360.0 |
245 |
3.21 |
3.570 |
15.84 |
0 |
0 |
3 |
4 |
| Merc 240D |
Merc 240D |
24.4 |
4 |
146.7 |
62 |
3.69 |
3.190 |
20.00 |
1 |
0 |
4 |
2 |
| Merc 230 |
Merc 230 |
22.8 |
4 |
140.8 |
95 |
3.92 |
3.150 |
22.90 |
1 |
0 |
4 |
2 |
| Merc 280 |
Merc 280 |
19.2 |
6 |
167.6 |
123 |
3.92 |
3.440 |
18.30 |
1 |
0 |
4 |
4 |
| Merc 280C |
Merc 280C |
17.8 |
6 |
167.6 |
123 |
3.92 |
3.440 |
18.90 |
1 |
0 |
4 |
4 |
| Merc 450SE |
Merc 450SE |
16.4 |
8 |
275.8 |
180 |
3.07 |
4.070 |
17.40 |
0 |
0 |
3 |
3 |
| Merc 450SL |
Merc 450SL |
17.3 |
8 |
275.8 |
180 |
3.07 |
3.730 |
17.60 |
0 |
0 |
3 |
3 |
| Merc 450SLC |
Merc 450SLC |
15.2 |
8 |
275.8 |
180 |
3.07 |
3.780 |
18.00 |
0 |
0 |
3 |
3 |
| Cadillac Fleetwood |
Cadillac Fleetwood |
10.4 |
8 |
472.0 |
205 |
2.93 |
5.250 |
17.98 |
0 |
0 |
3 |
4 |
carb = cars.carb
carb.value_counts()
4 10
2 10
1 7
3 3
8 1
6 1
Name: carb, dtype: int64
cars_cat = cars[['cyl','vs','am','gear','carb']]
cars_cat.head()
|
cyl |
vs |
am |
gear |
carb |
| car_names |
|
|
|
|
|
| Mazda RX4 |
6 |
0 |
1 |
4 |
4 |
| Mazda RX4 Wag |
6 |
0 |
1 |
4 |
4 |
| Datsun 710 |
4 |
1 |
1 |
4 |
1 |
| Hornet 4 Drive |
6 |
1 |
0 |
3 |
1 |
| Hornet Sportabout |
8 |
0 |
0 |
3 |
2 |
gears_group = cars_cat.groupby('gear')
gears_group.describe()
|
cyl |
vs |
... |
am |
carb |
|
count |
mean |
std |
min |
25% |
50% |
75% |
max |
count |
mean |
... |
75% |
max |
count |
mean |
std |
min |
25% |
50% |
75% |
max |
| gear |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 3 |
15.0 |
7.466667 |
1.187234 |
4.0 |
8.0 |
8.0 |
8.0 |
8.0 |
15.0 |
0.200000 |
... |
0.0 |
0.0 |
15.0 |
2.666667 |
1.175139 |
1.0 |
2.0 |
3.0 |
4.0 |
4.0 |
| 4 |
12.0 |
4.666667 |
0.984732 |
4.0 |
4.0 |
4.0 |
6.0 |
6.0 |
12.0 |
0.833333 |
... |
1.0 |
1.0 |
12.0 |
2.333333 |
1.302678 |
1.0 |
1.0 |
2.0 |
4.0 |
4.0 |
| 5 |
5.0 |
6.000000 |
2.000000 |
4.0 |
4.0 |
6.0 |
8.0 |
8.0 |
5.0 |
0.200000 |
... |
1.0 |
1.0 |
5.0 |
4.400000 |
2.607681 |
2.0 |
2.0 |
4.0 |
6.0 |
8.0 |
3 rows × 32 columns
cars['group'] = pd.Series(cars.gear,dtype="category")
cars['group'].dtypes
CategoricalDtype(categories=[3, 4, 5], ordered=False)
cars['group'].value_counts()
3 15
4 12
5 5
Name: group, dtype: int64
Describing categorical data with crosstabs
pd.crosstab(cars['am'],cars['gear'])
| gear |
3 |
4 |
5 |
| am |
|
|
|
| 0 |
15 |
4 |
0 |
| 1 |
0 |
8 |
5 |