1.Series介紹

Pandas模塊的數據結構主要有兩種：1.Series 2.DataFrame

Series 是一維數組，基于Numpy的ndarray 結構

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

2.Series創建

import Pandas as pd 
import numpy as np

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

參數為list ,index為可選參數，若不填寫則默認為index從0開始

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

輸出結果為：

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 計算、描述性統計

?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:

Returns:

Series：Series ordered by values.

obj.sort_values()

輸出結果為：

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:

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')

輸出結果為：

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

總結