Pandas count()与values_count()用法

时间:2022-06-03 Elvirangel 人气:5

Pandas count()与values_count()用法

count()

values_count()在指定的统计的列名上

结果多了该列：

对比：

Pandas:count()与value_counts()对比

1. Series.value_counts(self, normalize=False, sort=True, ascending=False, bins=None, dropna=True)

返回一个包含所有值及其数量的 Series。且为降序输出，即数量最多的第一行输出。

参数含义如下：

Parameters:	normalize : boolean, default False If True then the object returned will contain the relative frequencies of the unique values. sort : boolean, default True Sort by frequencies. ascending : boolean, default False Sort in ascending order. bins : integer, optional Rather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data. dropna : boolean, default True Don’t include counts of NaN.
Returns:	Series

Parameters:

normalize : boolean, default False

If True then the object returned will contain the relative frequencies of the unique values.

sort : boolean, default True

Sort by frequencies.

ascending : boolean, default False

Sort in ascending order.

bins : integer, optional

Rather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data.

dropna : boolean, default True

Don’t include counts of NaN.

Returns:

Series

举例如下：

import pandas as pd
index = pd.Index([3, 1, 2, 3, 4, np.nan])
index.value_counts()
 
"""
输出为：
3.0    2
4.0    1
2.0    1
1.0    1
dtype: int64
"""

如果 normalize 为 True的话，统计的结果会相加 = 1：

import pandas as pd
s = pd.Series([3, 1, 2, 3, 4, np.nan])
s.value_counts(normalize=True)
 
"""
输出为：
3.0    0.4
4.0    0.2
2.0    0.2
1.0    0.2
dtype: float64
"""

2. Series.count(self, level=None)

返回非空值的数量。若是在 CSV 文件中可用来统计行数，如：

import pandas as pd
file = pd.read_csv('test.csv')
print(file['A'].count())
# 此时输出的即是 A 列的行数

参数含义如下：

Parameters:	level : int or level name, default None If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series.
Returns:	int or Series (if level specified) Number of non-null values in the Series.

Parameters:

level : int or level name, default None

If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a smaller Series.

Returns:

int or Series (if level specified)

Number of non-null values in the Series.

举例如下：

import pands as pd
s = pd.Series([0.0, 1.0, np.nan])
s.count()
# 此时输出为 2

这就是两者的区别和各自的用途。

以上为个人经验，希望能给大家一个参考，也希望大家多多支持。

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