Python pandas

Python

Published

July 3, 2023

pandas - Tutorials
Access R in Python chunk:

r["mtcars"]

                      mpg  cyl   disp     hp  drat  ...   qsec   vs   am  gear  carb
Mazda RX4            21.0  6.0  160.0  110.0  3.90  ...  16.46  0.0  1.0   4.0   4.0
Mazda RX4 Wag        21.0  6.0  160.0  110.0  3.90  ...  17.02  0.0  1.0   4.0   4.0
Datsun 710           22.8  4.0  108.0   93.0  3.85  ...  18.61  1.0  1.0   4.0   1.0
Hornet 4 Drive       21.4  6.0  258.0  110.0  3.08  ...  19.44  1.0  0.0   3.0   1.0
Hornet Sportabout    18.7  8.0  360.0  175.0  3.15  ...  17.02  0.0  0.0   3.0   2.0
Valiant              18.1  6.0  225.0  105.0  2.76  ...  20.22  1.0  0.0   3.0   1.0
Duster 360           14.3  8.0  360.0  245.0  3.21  ...  15.84  0.0  0.0   3.0   4.0
Merc 240D            24.4  4.0  146.7   62.0  3.69  ...  20.00  1.0  0.0   4.0   2.0
Merc 230             22.8  4.0  140.8   95.0  3.92  ...  22.90  1.0  0.0   4.0   2.0
Merc 280             19.2  6.0  167.6  123.0  3.92  ...  18.30  1.0  0.0   4.0   4.0
Merc 280C            17.8  6.0  167.6  123.0  3.92  ...  18.90  1.0  0.0   4.0   4.0
Merc 450SE           16.4  8.0  275.8  180.0  3.07  ...  17.40  0.0  0.0   3.0   3.0
Merc 450SL           17.3  8.0  275.8  180.0  3.07  ...  17.60  0.0  0.0   3.0   3.0
Merc 450SLC          15.2  8.0  275.8  180.0  3.07  ...  18.00  0.0  0.0   3.0   3.0
Cadillac Fleetwood   10.4  8.0  472.0  205.0  2.93  ...  17.98  0.0  0.0   3.0   4.0
Lincoln Continental  10.4  8.0  460.0  215.0  3.00  ...  17.82  0.0  0.0   3.0   4.0
Chrysler Imperial    14.7  8.0  440.0  230.0  3.23  ...  17.42  0.0  0.0   3.0   4.0
Fiat 128             32.4  4.0   78.7   66.0  4.08  ...  19.47  1.0  1.0   4.0   1.0
Honda Civic          30.4  4.0   75.7   52.0  4.93  ...  18.52  1.0  1.0   4.0   2.0
Toyota Corolla       33.9  4.0   71.1   65.0  4.22  ...  19.90  1.0  1.0   4.0   1.0
Toyota Corona        21.5  4.0  120.1   97.0  3.70  ...  20.01  1.0  0.0   3.0   1.0
Dodge Challenger     15.5  8.0  318.0  150.0  2.76  ...  16.87  0.0  0.0   3.0   2.0
AMC Javelin          15.2  8.0  304.0  150.0  3.15  ...  17.30  0.0  0.0   3.0   2.0
Camaro Z28           13.3  8.0  350.0  245.0  3.73  ...  15.41  0.0  0.0   3.0   4.0
Pontiac Firebird     19.2  8.0  400.0  175.0  3.08  ...  17.05  0.0  0.0   3.0   2.0
Fiat X1-9            27.3  4.0   79.0   66.0  4.08  ...  18.90  1.0  1.0   4.0   1.0
Porsche 914-2        26.0  4.0  120.3   91.0  4.43  ...  16.70  0.0  1.0   5.0   2.0
Lotus Europa         30.4  4.0   95.1  113.0  3.77  ...  16.90  1.0  1.0   5.0   2.0
Ford Pantera L       15.8  8.0  351.0  264.0  4.22  ...  14.50  0.0  1.0   5.0   4.0
Ferrari Dino         19.7  6.0  145.0  175.0  3.62  ...  15.50  0.0  1.0   5.0   6.0
Maserati Bora        15.0  8.0  301.0  335.0  3.54  ...  14.60  0.0  1.0   5.0   8.0
Volvo 142E           21.4  4.0  121.0  109.0  4.11  ...  18.60  1.0  1.0   4.0   2.0

[32 rows x 11 columns]

Access Python object in R chunk

xyz = 10

library(reticulate)
py$xyz

[1] 10

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

iris = pd.read_csv('iris.csv')
iris['Sepal.Length'].describe()

count    150.000000
mean       5.843333
std        0.828066
min        4.300000
25%        5.100000
50%        5.800000
75%        6.400000
max        7.900000
Name: Sepal.Length, dtype: float64

iris['Sepal.Width'].describe()

count    150.000000
mean       3.057333
std        0.435866
min        2.000000
25%        2.800000
50%        3.000000
75%        3.300000
max        4.400000
Name: Sepal.Width, dtype: float64

iris['total_length'] = iris['Sepal.Length'] + iris['Sepal.Width']
iris['total_length'].describe()

count    150.000000
mean       8.900667
std        0.889272
min        6.800000
25%        8.300000
50%        8.850000
75%        9.575000
max       11.700000
Name: total_length, dtype: float64

# two identical results:
iris.loc[iris['total_length'] > 10, 'total_length']

15     10.1
50     10.2
102    10.1
105    10.6
107    10.2
109    10.8
117    11.5
118    10.3
120    10.1
122    10.5
125    10.4
129    10.2
130    10.2
131    11.7
135    10.7
Name: total_length, dtype: float64

iris[iris['total_length'] > 10]['total_length']

15     10.1
50     10.2
102    10.1
105    10.6
107    10.2
109    10.8
117    11.5
118    10.3
120    10.1
122    10.5
125    10.4
129    10.2
130    10.2
131    11.7
135    10.7
Name: total_length, dtype: float64


iris.loc[iris['total_length'] > 10, 'total_length'] = 100