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Python數據處理之pd.Series()函數的基本使用

編輯：Python

1.Series介紹

2.Series創建

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

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

3 Series基本屬性

4 索引

5 計算、描述性統計

6 排序

總結

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，基於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 np1.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:

ParametersDescription axis{0 or ‘index’}, default 0，Axis to direct sorting. The value ‘index’ is accepted for compatibility with DataFrame.sort_values.ascendinbool, default True，If True, sort values in ascending order, otherwise descending.inplacebool, 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:

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 groupsnumeric_onlybool, 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 ascendingascendingbool, default True Whether or not the elements should be ranked in ascending order.pctbool, 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NaNobj.rank(method='dense') # obj.rank(method='min')# obj.rank(method='max')# obj.rank(method='first')# obj.rank(method='dense')

輸出結果為：