Python数据处理之pd.Series()函数的基本使用

Ailis ·

更新时间:2024-11-10

· 1023 次阅读

1.Series介绍

2.Series创建

1.pd.Series([list],index=[list])

2.pd.Series(np.arange())

3 Series基本属性

4 索引

5 计算、描述性统计

6 排序

总结

1.Series介绍

Pandas模块的数据结构主要有两种：1.Series 2.DataFrame

Series 是一维数组，基于Numpy的ndarray 结构

Series([data, index, dtype, name, copy, …])    
# One-dimensional ndarray with axis labels (including time series).

2.Series创建

import Pandas as pd 
import numpy as np

1.pd.Series([list],index=[list])

参数为list ,index为可选参数，若不填写则默认为index从0开始

obj = pd.Series([4, 7, -5, 3, 7, np.nan])
obj

输出结果为：

0 4.0
1 7.0
2 -5.0
3 3.0
4 7.0
5 NaN
dtype: float64

2.pd.Series(np.arange())

arr = np.arange(6)
s = pd.Series(arr)
s

输出结果为：

0 0
1 1
2 2
3 3
4 4
5 5
dtype: int32

pd.Series({dict})
d = {'a':10,'b':20,'c':30,'d':40,'e':50}
s = pd.Series(d)
s

输出结果为：

a 10
b 20
c 30
d 40
e 50
dtype: int64

可以通过DataFrame中某一行或者某一列创建序列

3 Series基本属性

Series.values：Return Series as ndarray or ndarray-like depending on the dtype

obj.values
# array([ 4.,  7., -5.,  3.,  7., nan])

Series.index:The index (axis labels) of the Series.

obj.index
# RangeIndex(start=0, stop=6, step=1)

Series.name:Return name of the Series.

4 索引

Series.loc:Access a group of rows and columns by label(s) or a boolean array.

Series.iloc:Purely integer-location based indexing for selection by position.

5 计算、描述性统计

Series.value_counts:Return a Series containing counts of unique values.

index = ['Bob', 'Steve', 'Jeff', 'Ryan', 'Jeff', 'Ryan'] 
obj = pd.Series([4, 7, -5, 3, 7, np.nan],index = index)
obj.value_counts()

输出结果为：

7.0 2
3.0 1
-5.0 1
4.0 1
dtype: int64

6 排序

Series.sort_values

Series.sort_values(self, axis=0, ascending=True, inplace=False, kind='quicksort', na_position='last')

Parameters:

ParametersDescription

axis	{0 or ‘index’}, default 0，Axis to direct sorting. The value ‘index’ is accepted for compatibility with DataFrame.sort_values.
ascendin	bool, default True，If True, sort values in ascending order, otherwise descending.
inplace	bool, default FalseIf True, perform operation in-place.
kind	{‘quicksort’, ‘mergesort’ or ‘heapsort’}, default ‘quicksort’Choice of sorting algorithm. See also numpy.sort() for more information. ‘mergesort’ is the only stable algorithm.
na_position	{‘first’ or ‘last’}, default ‘last’，Argument ‘first’ puts NaNs at the beginning, ‘last’ puts NaNs at the end.

Returns:

Series：Series ordered by values.

obj.sort_values()

输出结果为：

Jeff -5.0
Ryan 3.0
Bob 4.0
Steve 7.0
Jeff 7.0
Ryan NaN
dtype: float64

Series.rank

Series.rank(self, axis=0, method='average', numeric_only=None, na_option='keep', ascending=True, pct=False)[source]

Parameters:

ParametersDescription

axis	{0 or ‘index’, 1 or ‘columns’}, default 0Index to direct ranking.
method	{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’How to rank the group of records that have the same value (i.e. ties): average， average rank of the group; min: lowest rank in the group; max: highest rank in the group; first: ranks assigned in order they appear in the array; dense: like ‘min’, but rank always increases by 1,between groups
numeric_only	bool, optional,For DataFrame objects, rank only numeric columns if set to True.
na_option	{‘keep’, ‘top’, ‘bottom’}, default ‘keep’, How to rank NaN values:;keep: assign NaN rank to NaN values; top: assign smallest rank to NaN values if ascending; bottom: assign highest rank to NaN values if ascending
ascending	bool, default True Whether or not the elements should be ranked in ascending order.
pct	bool, default False Whether or not to display the returned rankings in percentile form.

Returns:

same type as caller :Return a Series or DataFrame with data ranks as values.

# obj.rank()            #从大到小排，NaN还是NaN
obj.rank(method='dense')  
# obj.rank(method='min')
# obj.rank(method='max')
# obj.rank(method='first')
# obj.rank(method='dense')

输出结果为：