Statistical calculation function training & pandas

Tools/materials:
"""jupyter notebook
Python3"""

Statistical calculation function exercise

import pandas as pd
import numpy as np

1. Create a DataFrame object with 3 rows and 5 columns of random integers, ranging from 0-9

df1 = pd.DataFrame(np.random.randint(0,10,(3,5)))
df1

	0	1	2	3	4
0	9	6	5	7	6
1	4	5	4	7	5
2	9	1	9	1	7

2. Count the maximum, minimum, cumulative sum of each row by row

#最大值
df1.max(axis=1)

0    9
1    7
2    9
dtype: int32

#最小值
df1.min(axis=1)

0    5
1    4
2    1
dtype: int32

#累计和
df1.mean(axis=1)

0    6.6
1    5.0
2    5.4
dtype: float64

3. Count the sum, average, and cumulative product of each column by column

Note: The parameters are calculated by column by default

df1.sum()

0    22
1    12
2    18
3    15
4    18
dtype: int64

df1.mean()

0    7.333333
1    4.000000
2    6.000000
3    5.000000
4    6.000000
dtype: float64

#累计积
df1.cumprod()

	0	1	2	3	4
0	9	6	5	7	6
1	36	30	20	49	30
2	324	30	180	49	210

4. Use describe to output multiple statistical functions

df1.describe()

	0	1	2	3	4
count	3.000000	3.000000	3.000000	3.000000	3.0
mean	7.333333	4.000000	6.000000	5.000000	6.0
std	2.886751	2.645751	2.645751	3.464102	1.0
min	4.000000	1.000000	4.000000	1.000000	5.0
25%	6.500000	3.000000	4.500000	4.000000	5.5
50%	9.000000	5.000000	5.000000	7.000000	6.0
75%	9.000000	5.500000	7.000000	7.000000	6.5
max	9.000000	6.000000	9.000000	7.000000	7.0