import numpy as np
import pandas as pd
df=pd.DataFrame([[1.4,np.nan],[7.1,-4.5],
[np.nan,np.nan],[0.75,-1.3]],
index=['a','b','c','d'],
columns=['one','two'])
df
| one | two |
---|
a | 1.40 | NaN |
---|
b | 7.10 | -4.5 |
---|
c | NaN | NaN |
---|
d | 0.75 | -1.3 |
---|
#默认按列求和
df.sum()
one 9.25
two -5.80
dtype: float64
# 按行求和
df.sum(axis=1) # skipna = False
a 1.40
b 2.60
c 0.00
d -0.55
dtype: float64
df.idxmax()
one b
two d
dtype: object
df.cumsum()
| one | two |
---|
a | 1.40 | NaN |
---|
b | 8.50 | -4.5 |
---|
c | NaN | NaN |
---|
d | 9.25 | -5.8 |
---|
# 汇总统计
df.describe()
| one | two |
---|
count | 3.000000 | 2.000000 |
---|
mean | 3.083333 | -2.900000 |
---|
std | 3.493685 | 2.262742 |
---|
min | 0.750000 | -4.500000 |
---|
25% | 1.075000 | -3.700000 |
---|
50% | 1.400000 | -2.900000 |
---|
75% | 4.250000 | -2.100000 |
---|
max | 7.100000 | -1.300000 |
---|
s1 = pd.Series(['a','a','b','c']*4)
s1
0 a
1 a
2 b
3 c
4 a
5 a
6 b
7 c
8 a
9 a
10 b
11 c
12 a
13 a
14 b
15 c
dtype: object
s1.describe()
count 16
unique 3
top a
freq 8
dtype: object
标签:4.5,06,df,dtype,nan,two,np,Panda,描述
来源: https://blog.csdn.net/qq_53535048/article/details/121072170