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

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