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
总结
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