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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-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: >
