import numpy as np
import pandas as pd
df_obj = pd.DataFrame(np.random.randn(5,4), columns = ['a', 'b', 'c', 'd'])
df_obj
| a | b | c | d | |
|---|---|---|---|---|
| 0 | -0.281460 | -0.458650 | -1.102619 | 0.226367 |
| 1 | -0.045202 | -0.801324 | -2.910940 | -1.249168 |
| 2 | 1.260569 | -0.262852 | 1.348291 | -0.103332 |
| 3 | -0.688225 | -0.658354 | -1.498003 | -1.288892 |
| 4 | -0.415888 | 2.523040 | 0.107214 | -0.849228 |
df_obj.sum()
a -0.170205
b 0.341860
c -4.056056
d -3.264252
dtype: float64
df_obj.max()
a 1.260569
b 2.523040
c 1.348291
d 0.226367
dtype: float64
df_obj.min(axis=1)
0 -1.102619
1 -2.910940
2 -0.262852
3 -1.498003
4 -0.849228
dtype: float64
df_obj.describe()
| a | b | c | d | |
|---|---|---|---|---|
| count | 5.000000 | 5.000000 | 5.000000 | 5.000000 |
| mean | -0.034041 | 0.068372 | -0.811211 | -0.652850 |
| std | 0.760118 | 1.387206 | 1.618056 | 0.684416 |
| min | -0.688225 | -0.801324 | -2.910940 | -1.288892 |
| 25% | -0.415888 | -0.658354 | -1.498003 | -1.249168 |
| 50% | -0.281460 | -0.458650 | -1.102619 | -0.849228 |
| 75% | -0.045202 | -0.262852 | 0.107214 | -0.103332 |
| max | 1.260569 | 2.523040 | 1.348291 | 0.226367 |