Series是帶標(biāo)簽的一維數(shù)組,可存儲(chǔ)整數(shù)、浮點(diǎn)數(shù)、字符串、Python 對(duì)象等類型的數(shù)據(jù),軸標(biāo)簽統(tǒng)稱為索引,下面這篇文章主要給大家介紹了關(guān)于Python數(shù)據(jù)處理之pd.Series()函數(shù)的基本使用,需要的朋友可以參考下

1.Series介紹

Pandas模塊的數(shù)據(jù)結(jié)構(gòu)主要有兩種：1.Series 2.DataFrame

Series 是一維數(shù)組，基于Numpy的ndarray 結(jié)構(gòu)

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

2.Series創(chuàng)建

import Pandas as pd 
import numpy as np

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

參數(shù)為list ,index為可選參數(shù)，若不填寫則默認(rèn)為index從0開始

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

輸出結(jié)果為：

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

輸出結(jié)果為：

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

輸出結(jié)果為：

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

可以通過DataFrame中某一行或者某一列創(chuàng)建序列

3 Series基本屬性

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 計(jì)算、描述性統(tǒng)計(jì)

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()

輸出結(jié)果為：

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:

Parameters	Description
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()

輸出結(jié)果為：

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:

Parameters	Description
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()            #從大到小排，NaN還是NaN
obj.rank(method='dense')  
# obj.rank(method='min')
# obj.rank(method='max')
# obj.rank(method='first')
# obj.rank(method='dense')

輸出結(jié)果為：

Bob 3.0
Steve 4.0
Jeff 1.0
Ryan 2.0
Jeff 4.0
Ryan NaN
dtype: float64

總結(jié)

到此這篇關(guān)于Python數(shù)據(jù)處理之pd.Series()函數(shù)的基本使用的文章就介紹到這了,更多相關(guān)Python pd.Series()函數(shù)內(nèi)容請(qǐng)搜索腳本之家以前的文章或繼續(xù)瀏覽下面的相關(guān)文章希望大家以后多多支持腳本之家！

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