pandas知识点汇总

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## pandas基础知识汇总

1.时间序列

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from datetime import datetime
now=datetime.now()
now
datetime.datetime(2018, 11, 18, 16, 44, 4, 405600)
print(now.strftime('%Y-%m-%d'))
print(datetime.strptime('7/6/2018','%m/%d/%Y'))
print(now.strftime('%X'))
2018-11-18
2018-07-06 00:00:00
16:44:04
dates=pd.date_range('11/1/2018',periods=50,freq='W-WED')
long_df=pd.DataFrame(np.random.randn(50,4),index=dates,columns=list('ABCD'))
long_df.head(10)
A B C D
2018-11-07 0.215536 0.855986 0.737170 -0.440150
2018-11-14 -0.477099 0.467430 -0.107105 0.941922
2018-11-21 0.052926 -0.671084 0.219058 -0.350776
2018-11-28 -1.449668 0.003958 1.065875 -0.277673
2018-12-05 1.371631 0.542839 0.071466 0.609508
2018-12-12 0.322176 1.335534 -0.423240 -0.111549
2018-12-19 -0.564089 0.262918 0.477552 0.018652
2018-12-26 -0.490212 0.382492 -0.858712 -0.920786
2019-01-02 1.630409 -0.740542 1.296362 0.376437
2019-01-09 1.460070 -0.449293 -0.783725 -1.098911
resample=long_df.resample('M').mean()
resample
A B C D
2018-11-30 -0.414576 0.164073 0.478750 -0.031669
2018-12-31 0.159876 0.630946 -0.183234 -0.101044
2019-01-31 0.092189 -0.225606 0.251072 -0.456075
2019-02-28 -0.124615 -0.467522 -0.142258 0.195602
2019-03-31 -0.294693 -0.014264 0.725285 1.291576
2019-04-30 0.182648 0.231022 -0.458572 0.294329
2019-05-31 0.317648 0.060677 0.297406 -0.035691
2019-06-30 0.407404 -0.198072 -0.461785 1.074969
2019-07-31 -0.245908 0.150161 0.526564 -0.082258
2019-08-31 0.046819 -0.227364 -0.684359 0.033979
2019-09-30 -0.834454 1.186670 0.653583 -0.306585
2019-10-31 -0.436990 -0.460347 0.040175 0.681903
pd.date_range('11/18/2018',periods=10,freq='2h30min')
DatetimeIndex(['2018-11-18 00:00:00', '2018-11-18 02:30:00',
               '2018-11-18 05:00:00', '2018-11-18 07:30:00',
               '2018-11-18 10:00:00', '2018-11-18 12:30:00',
               '2018-11-18 15:00:00', '2018-11-18 17:30:00',
               '2018-11-18 20:00:00', '2018-11-18 22:30:00'],
              dtype='datetime64[ns]', freq='150T')
type(resample)
pandas.core.resample.DatetimeIndexResampler
ts=pd.Series(np.arange(10),index=pd.date_range('11/18/2018',periods=10,freq='T'))
ts
2018-11-18 00:00:00    0
2018-11-18 00:01:00    1
2018-11-18 00:02:00    2
2018-11-18 00:03:00    3
2018-11-18 00:04:00    4
2018-11-18 00:05:00    5
2018-11-18 00:06:00    6
2018-11-18 00:07:00    7
2018-11-18 00:08:00    8
2018-11-18 00:09:00    9
Freq: T, dtype: int32
#pay attention to the parameter 'closed'
ts.resample('3min',closed='left',label='left').sum()
2018-11-18 00:00:00     3
2018-11-18 00:03:00    12
2018-11-18 00:06:00    21
2018-11-18 00:09:00     9
Freq: 3T, dtype: int32
ts.resample('3min').ohlc()
open high low close
2018-11-18 00:00:00 0 2 0 2
2018-11-18 00:03:00 3 5 3 5
2018-11-18 00:06:00 6 8 6 8
2018-11-18 00:09:00 9 9 9 9
long_df.plot()

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## 滑窗函数
fig,axes=plt.subplots(1,3,figsize=(20,4))
long_df['A'].plot(ax=axes[0])
long_df['A'].rolling(window=10).mean().plot(ax=axes[0],title='A_10_mean')
long_df['B'].plot(ax=axes[1])
long_df['B'].rolling(window=10).sum().plot(ax=axes[1],title='B_10_sum')
long_df['C'].plot(ax=axes[2])
long_df['C'].rolling(window=10).quantile(quantile=0.8).plot(ax=axes[2],title='C_10_quantile')

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#corr
from pylab import mpl 
mpl.rcParams['font.sans-serif'] = ['SimHei'] # 指定默认字体
mpl.rcParams['axes.unicode_minus'] = False # 解决保存图像是负号'-'显示为方块的问题
long_df['B'].rolling(window=10).corr(long_df['A']).plot(style='ro--',grid=True,title='二元函数相关系数')

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2.matplotlib绘图

long_df['A'].plot(kind='kde',style='g')

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pd.plotting.scatter_matrix(long_df,diagonal='kde',color='r')

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df=pd.DataFrame(np.random.randn(6,4),index='one two three four five six'.split(' '),columns=list('ABCD'))
df_normal=abs(df).div(abs(df).sum(1),axis=0)
df_normal.plot(kind='barh',stacked=True)
abs(df).sum(1)
one      3.989060
two      1.160160
three    2.087209
four     2.680116
five     4.452365
six      2.298789
dtype: float64

技术分享图片

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