Python数据处理之pd.Series()函数的基本使用

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:

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:

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：

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

总结

到此这篇关于Python数据处理之pd.Series()函数的基本使用的文章就介绍到这了,更多相关Python pd.Series()函数内容请搜索编程网以前的文章或继续浏览下面的相关文章希望大家以后多多支持编程网！