目录
- 1.Series介绍
- 2.Series创建
- 1.pd.Series([list],index=[list])
- 2.pd.Series(np.arange())
- 3 Series基本属性
- 4 索引
- 5 计算、描述性统计
- 6 排序
- 总结
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:
| 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()
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:
| 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() #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
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