参数: 
normalize : 布尔值,默认为False,如果是True的话,就会包含该值出现次数的频率. 
sort : 布尔值,默认为True.排序控制. 
ascending : 布尔值,默认为False,以升序排序 
bins : integer, optional 
Rather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data 
dropna : 布尔型,默认为True,表示不包括NaN

2.pandas.DataFrame.count

DataFrame.count(axis=0, level=None, numeric_only=False) 
Return Series with number of non-NA/null observations over requested axis. Works with non-floating point data as well (detects NaN and None)

Parameters: 
axis : {0 or ‘index’, 1 or ‘columns’}, default 0 
0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise 
level : int or level name, default None 
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a DataFrame 
numeric_only : boolean, default False 
Include only float, int, boolean data 
Returns: 
count : Series (or DataFrame if level specified)

参数: 
axis : {index (0), columns (1)} 
skipna : 布尔值,默认为True.表示跳过NaN值.如果整行/列都是NaN,那么结果也就是NaN 
level : int or level name, default None 
If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series 
numeric_only : boolean, default None 
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series. 
Returns: 
sum : Series or DataFrame (if level specified)

import numpy as np

import pandas as pd

df=pd.DataFrame(data=[[1.4,np.nan],[7.1,-4.5],[np.nan,np.nan],[0.75,-1.3]],                index=["a","b","c","d"],

columns=["one","two"])

print("df:")

print(df)

#直接使用sum()方法,返回一个列求和的Series,自动跳过NaN值

print("df.sum()")

print(df.sum())

#当轴为1.就会按行求和

print("df.sum(axis=1)")

print(df.sum(axis=1))

#选择skipna=False可以禁用跳过Nan值

print("df.sum(axis=1,skipna=False):")

print(df.sum(axis=1,skipna=False))

import numpy as np

import pandas as pd

df=pd.DataFrame(data=[[1.4,np.nan],[7.1,-4.5],[np.nan,np.nan],[0.75,-1.3]],                
index=["a","b","c","d"],

columns=["one","two"])

print("df:")

print(df)

#直接使用mean()方法,返回一个列求平均数的Series,自动跳过NaN值

print("df.mean()")

print(df.mean())

#当轴为1.就会按行求平均数

print("df.mean(axis=1)")

print(df.mean(axis=1))

#选择skipna=False可以禁用跳过Nan值

print("df.mean(axis=1,skipna=False):")

print(df.mean(axis=1,skipna=False))