3.4-plot快速可视化

In [ ]:

import pandas as pd
import numpy as np

In [ ]:

# dataframe.plot功能

In [ ]:

# 3.4.2 单样本：生成随机数时间序列 --> 可视化数据样本 --> 模拟处理、绘制
df = np.random.randn(1000)
df 

Out[ ]:

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        4.45373893e-02,  2.72836302e+00,  6.37130299e-01, -1.15852794e+00,
       -2.44524943e-01, -9.25677150e-01, -8.62053970e-02, -6.23954455e-01,
        7.48051441e-01,  6.18895521e-01, -2.97749197e-01, -1.63370209e+00,
       -8.54478825e-01,  9.71659088e-01,  5.01085199e-01, -1.13714556e+00,
       -1.90922863e-01, -1.10678774e+00, -1.06454977e-01,  1.77171249e-01,
       -7.95575707e-01, -1.13998647e+00,  5.09979459e-01,  8.81022791e-01,
        3.20799418e-01,  5.66690135e-01, -1.33643998e+00,  5.89393367e-01,
       -2.28337122e+00,  3.55632160e-01,  4.23517314e-01, -1.74263030e+00,
       -1.31002901e-01, -2.79686369e-01, -2.08660885e+00,  2.36712541e-01,
       -1.48180840e-02, -6.59384871e-01, -2.29125418e+00, -1.57824645e+00,
        8.21347516e-01, -4.90343565e-02,  1.57413318e-01,  3.77484976e-01,
       -7.74693321e-01, -1.19042949e+00, -8.73369101e-01, -9.38971687e-01,
       -6.83662203e-01, -1.86744893e-01,  1.55888452e+00, -7.99250088e-01,
       -2.34050077e+00, -3.69369611e-01,  1.38948328e+00, -5.59350402e-01,
       -3.13672723e-01,  6.15523785e-01,  6.60105717e-01,  7.54335531e-01,
        2.81669511e-01, -1.74163698e+00, -1.56674142e-01,  8.94687332e-01,
        1.68606125e+00,  1.82549837e-01, -1.28321866e+00, -6.94477353e-01,
       -3.17129289e-02, -7.62347282e-01, -3.74959966e-01,  6.99585427e-01,
        1.40364051e+00,  1.35472457e+00,  1.10384841e-01,  1.87546332e-01,
       -1.18693035e-01, -3.25954809e-01, -1.45630428e+00,  8.71587208e-01,
        5.89001817e-01, -1.26791035e+00, -1.40390226e+00, -1.25535605e+00,
       -8.25571484e-02, -1.55922022e+00, -1.57650162e+00,  2.06329433e-03,
        1.08090507e+00, -3.88061895e-01, -5.19225711e-01,  4.18825309e-01,
        5.35899854e-01, -5.42895430e-01,  4.03414165e-01, -7.73296988e-01,
        1.28807942e+00,  1.12809466e+00, -8.05824033e-01,  1.41178968e-01,
       -1.21947146e+00,  7.76484486e-01,  1.58405500e+00, -1.79341595e+00,
       -7.10592737e-02,  1.02263793e+00,  1.46480170e+00,  5.97755502e-01,
       -5.72605739e-01,  6.49855243e-01,  2.02061424e-01, -3.50829077e-01,
       -9.51151069e-02,  1.02745859e+00,  9.63182987e-01, -2.97281242e+00,
        3.72537399e-01,  2.91984879e-01, -1.29909186e-01,  1.87540759e+00,
        2.19685058e+00, -5.21279364e-01,  1.27749019e+00,  7.56587090e-01,
        2.49696175e-01, -5.60834259e-01, -2.04470346e-01, -1.12060820e+00,
       -7.61610391e-01,  3.66816005e-01,  9.21946735e-01, -5.12808796e-01,
        5.38636064e-01, -2.51775643e-01, -2.44525918e+00,  1.34554576e+00,
        9.18212510e-01,  4.88047184e-01, -1.53869418e-01, -5.78136453e-01,
       -9.94020084e-01, -5.71868289e-02,  1.91419741e-01, -1.88138918e-01,
        8.25927810e-01,  8.26632911e-02,  5.19735238e-01,  1.97712235e+00,
        2.99895522e-01,  3.95329591e-01, -3.25634950e-01,  2.47202575e-01,
       -1.69835178e-01,  1.24033328e-01, -1.61591981e+00, -7.28642245e-01,
        8.73660668e-03, -2.20195581e+00, -8.00379222e-02, -1.15543047e+00,
        1.70965825e+00, -7.83688016e-01,  1.18236530e+00,  3.17412372e-02,
        1.26303480e+00,  4.15565328e-01, -1.10274873e+00,  1.48421222e+00,
        3.56578140e-01,  1.72833077e+00, -6.66565595e-01, -7.37405803e-01,
       -1.52431458e+00,  1.29924130e+00,  8.87079385e-01,  2.60568470e-01,
        1.79793941e-01,  1.46838609e+00,  1.46438804e+00, -1.79247058e+00,
       -3.48880353e-01, -4.15002305e-03, -1.03171850e+00,  2.91636717e-01,
       -1.90745068e+00,  7.45420590e-01,  2.04729967e+00,  6.50453437e-01,
        1.82874326e+00, -6.92057690e-01,  3.59589574e-01, -1.19127096e+00,
        2.93786300e-01,  2.82529493e-01,  3.99095456e-01, -3.01668263e-01,
        8.55716096e-01, -7.36371474e-01, -1.49424280e+00, -5.72105104e-01,
        1.57993397e+00,  8.41332320e-01,  3.91845848e-01, -7.81317740e-01,
        1.37456890e+00,  6.72021358e-01, -1.66509853e+00,  1.32916912e-01,
        3.53776502e-01,  8.21282544e-01,  6.83509026e-01, -1.40955990e+00,
        1.59362823e+00,  1.10935599e+00,  8.22945999e-01,  4.01362612e-01,
        1.45198968e+00, -1.76974866e-01, -8.60610559e-01,  1.51908797e+00,
        5.38304351e-01, -1.60074606e+00,  7.48355979e-01,  7.85493799e-01,
        1.53054743e-01,  1.23870115e-01,  4.76671517e-02,  2.58486543e-01,
       -6.83672882e-01, -1.99656371e+00, -1.19383592e+00, -1.39868555e+00,
       -3.73121481e-01,  3.26015391e+00,  3.21637043e-01, -3.64609318e-01,
        1.86599158e-01, -3.68188627e-01,  5.72786569e-01, -5.51944039e-01,
       -3.16390206e-01,  1.40969955e+00,  2.02417917e-01, -7.47987235e-01,
       -8.19101212e-02,  1.96718171e+00,  4.46146841e-01,  1.52865477e+00,
       -1.22679442e+00, -5.35761425e-01,  3.80404013e-01, -2.23831449e-02,
       -1.57959273e+00, -1.04313714e+00,  6.23255952e-01,  3.68980246e-02,
       -2.46623014e+00, -1.72508821e+00,  5.92714919e-02,  1.94651156e-01,
        2.91403105e-01, -1.06622024e+00,  1.34673196e+00, -9.57300833e-01,
       -7.66648650e-01,  3.40900711e-01,  6.87646649e-01,  1.29554671e+00,
        1.18354858e-01,  1.41877761e+00, -2.38609762e-01, -3.93554916e-01])

In [ ]:

df = pd.DataFrame(df,index=pd.date_range("20200101",periods=1000))
df.plot()

Out[ ]:

<Axes: >

In [ ]:

df['cumsum'] = df.cumsum()
df

Out[ ]:

	0	cumsum
2020-01-01	1.980237	1.980237
2020-01-02	-0.729391	1.250847
2020-01-03	-1.211877	0.038970
2020-01-04	-0.944301	-0.905332
2020-01-05	0.843605	-0.061727
...	...	...
2022-09-22	1.295547	-21.183233
2022-09-23	0.118355	-21.064879
2022-09-24	1.418778	-19.646101
2022-09-25	-0.238610	-19.884711
2022-09-26	-0.393555	-20.278266

1000 rows × 2 columns

In [ ]:

df['cumsum'].plot()

Out[ ]:

<Axes: >

In [ ]:

# 3.4.3 多样本
pd.DataFrame(np.random.randn(1000)).hist()  # 直方图

Out[ ]:

array([[<Axes: title={'center': '0'}>]], dtype=object)

In [ ]:

df2 = pd.DataFrame(np.random.randn(1000,4), columns=['A','B','C','D'],index=pd.date_range('20200101',periods=1000))

In [ ]:

df2

Out[ ]:

	A	B	C	D
2020-01-01	-0.316267	-0.064917	0.135941	-1.683531
2020-01-02	-1.775707	1.260687	-0.112083	-0.946609
2020-01-03	0.271756	-1.231808	0.311042	0.299439
2020-01-04	-0.693046	-0.260170	-0.507994	-1.618444
2020-01-05	1.063937	1.523653	-1.130773	-0.760272
...	...	...	...	...
2022-09-22	-0.134058	0.651518	1.246924	1.916938
2022-09-23	-1.451481	0.076728	-1.271193	0.510011
2022-09-24	-0.692761	0.834845	-0.819874	0.154790
2022-09-25	1.500856	-0.364554	-0.896864	0.717778
2022-09-26	-0.563136	0.625665	0.168741	-1.570240

1000 rows × 4 columns

In [ ]:

df2.hist()

Out[ ]:

array([[<Axes: title={'center': 'A'}>, <Axes: title={'center': 'B'}>],
       [<Axes: title={'center': 'C'}>, <Axes: title={'center': 'D'}>]],
      dtype=object)

In [ ]:

df2.plot()

Out[ ]:

<Axes: >

In [ ]:

df2sum = df2.cumsum()
df2sum.plot()

Out[ ]:

<Axes: >

In [ ]:

posted @ 2023-10-18 15:22 凝视灵魂阅读(36) 评论(0) 收藏举报

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