数据概要¶

头部数据、尾部数据
索引、列名
查看数值
查看统计摘要

数据查询¶

列数据
行数据
行列切片
按值筛选
按条件筛选(布尔值)

其他¶

转置
排序

In [ ]:

import pandas as pd
import numpy as np

In [ ]:

# 创建一个dataframe：带时间戳的价格数据
dates = pd.date_range("20210101",periods=30,freq="M")
dates

Out[ ]:

DatetimeIndex(['2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30',
               '2021-05-31', '2021-06-30', '2021-07-31', '2021-08-31',
               '2021-09-30', '2021-10-31', '2021-11-30', '2021-12-31',
               '2022-01-31', '2022-02-28', '2022-03-31', '2022-04-30',
               '2022-05-31', '2022-06-30', '2022-07-31', '2022-08-31',
               '2022-09-30', '2022-10-31', '2022-11-30', '2022-12-31',
               '2023-01-31', '2023-02-28', '2023-03-31', '2023-04-30',
               '2023-05-31', '2023-06-30'],
              dtype='datetime64[ns]', freq='M')

In [ ]:

data = pd.DataFrame(np.random.randn(30,3),columns=list('ABC'),index=dates)
data

Out[ ]:

	A	B	C
2021-01-31	-1.005821	-0.747159	-0.590444
2021-02-28	0.106087	-0.611014	-2.492806
2021-03-31	0.923487	-1.901083	-1.139865
2021-04-30	0.045023	-0.501125	0.834619
2021-05-31	-0.015439	-0.328349	0.905197
2021-06-30	0.366951	-0.421883	1.579878
2021-07-31	1.337484	1.290041	-0.466970
2021-08-31	-0.373738	-0.220213	-0.529416
2021-09-30	0.740679	-0.795566	-0.392513
2021-10-31	-0.759147	0.166461	2.225352
2021-11-30	0.120085	-0.969381	0.050001
2021-12-31	-1.328895	0.311472	0.237954
2022-01-31	0.211936	0.477653	-0.097692
2022-02-28	0.135520	0.445589	1.909404
2022-03-31	0.876071	1.117198	0.629551
2022-04-30	0.863037	-1.707017	0.470066
2022-05-31	-0.979964	0.257285	0.898436
2022-06-30	-1.423223	0.259646	-0.650481
2022-07-31	1.580251	-0.314205	0.639193
2022-08-31	1.954733	-1.515528	0.143653
2022-09-30	-0.722134	0.845884	-0.299418
2022-10-31	-0.448377	-1.045969	0.244326
2022-11-30	-0.092980	-1.089742	0.561777
2022-12-31	2.820850	-0.080729	0.770422
2023-01-31	-1.482163	0.365914	1.351397
2023-02-28	-0.364066	-0.182885	-0.922139
2023-03-31	-0.589401	0.592518	-0.119778
2023-04-30	0.705069	0.808626	2.058423
2023-05-31	0.659801	1.853893	1.030405
2023-06-30	0.363107	-0.512096	0.169748

In [ ]:

# 头部数据
data.head()

Out[ ]:

	A	B	C
2021-01-31	-1.005821	-0.747159	-0.590444
2021-02-28	0.106087	-0.611014	-2.492806
2021-03-31	0.923487	-1.901083	-1.139865
2021-04-30	0.045023	-0.501125	0.834619
2021-05-31	-0.015439	-0.328349	0.905197

In [ ]:

# 头部前3条
data.head(3)

Out[ ]:

	A	B	C
2021-01-31	-1.005821	-0.747159	-0.590444
2021-02-28	0.106087	-0.611014	-2.492806
2021-03-31	0.923487	-1.901083	-1.139865

In [ ]:

# 尾部数据
data.tail()

Out[ ]:

	A	B	C
2023-02-28	-0.364066	-0.182885	-0.922139
2023-03-31	-0.589401	0.592518	-0.119778
2023-04-30	0.705069	0.808626	2.058423
2023-05-31	0.659801	1.853893	1.030405
2023-06-30	0.363107	-0.512096	0.169748

In [ ]:

# 尾部3条
data.tail(3)

Out[ ]:

	A	B	C
2023-04-30	0.705069	0.808626	2.058423
2023-05-31	0.659801	1.853893	1.030405
2023-06-30	0.363107	-0.512096	0.169748

In [ ]:

# 索引
data.index

Out[ ]:

DatetimeIndex(['2021-01-31', '2021-02-28', '2021-03-31', '2021-04-30',
               '2021-05-31', '2021-06-30', '2021-07-31', '2021-08-31',
               '2021-09-30', '2021-10-31', '2021-11-30', '2021-12-31',
               '2022-01-31', '2022-02-28', '2022-03-31', '2022-04-30',
               '2022-05-31', '2022-06-30', '2022-07-31', '2022-08-31',
               '2022-09-30', '2022-10-31', '2022-11-30', '2022-12-31',
               '2023-01-31', '2023-02-28', '2023-03-31', '2023-04-30',
               '2023-05-31', '2023-06-30'],
              dtype='datetime64[ns]', freq='M')

In [ ]:

# 列名
data.columns

Out[ ]:

Index(['A', 'B', 'C'], dtype='object')

In [ ]:

# 查看数值(array)
data.values

Out[ ]:

array([[-1.00582066, -0.74715913, -0.59044421],
       [ 0.10608719, -0.61101411, -2.49280605],
       [ 0.92348709, -1.901083  , -1.13986527],
       [ 0.04502284, -0.50112509,  0.83461856],
       [-0.01543869, -0.32834889,  0.90519706],
       [ 0.36695114, -0.42188302,  1.57987849],
       [ 1.33748358,  1.29004108, -0.46697028],
       [-0.37373787, -0.22021339, -0.52941598],
       [ 0.74067882, -0.79556616, -0.39251251],
       [-0.75914684,  0.16646147,  2.22535186],
       [ 0.12008512, -0.96938058,  0.05000075],
       [-1.32889545,  0.31147169,  0.23795395],
       [ 0.2119362 ,  0.47765278, -0.0976922 ],
       [ 0.13551963,  0.44558949,  1.90940387],
       [ 0.87607052,  1.11719757,  0.62955101],
       [ 0.8630371 , -1.70701661,  0.47006564],
       [-0.97996414,  0.25728477,  0.89843618],
       [-1.42322307,  0.25964647, -0.65048082],
       [ 1.580251  , -0.3142048 ,  0.63919311],
       [ 1.95473317, -1.51552846,  0.1436534 ],
       [-0.7221338 ,  0.84588397, -0.29941785],
       [-0.44837652, -1.04596934,  0.24432642],
       [-0.0929797 , -1.08974158,  0.56177711],
       [ 2.82084974, -0.08072931,  0.77042241],
       [-1.48216309,  0.36591366,  1.35139741],
       [-0.3640665 , -0.18288453, -0.92213874],
       [-0.58940142,  0.59251817, -0.11977769],
       [ 0.70506868,  0.80862606,  2.05842293],
       [ 0.65980106,  1.85389319,  1.03040533],
       [ 0.36310659, -0.51209611,  0.16974761]])

In [ ]:

data.to_numpy()

Out[ ]:

array([[-1.00582066, -0.74715913, -0.59044421],
       [ 0.10608719, -0.61101411, -2.49280605],
       [ 0.92348709, -1.901083  , -1.13986527],
       [ 0.04502284, -0.50112509,  0.83461856],
       [-0.01543869, -0.32834889,  0.90519706],
       [ 0.36695114, -0.42188302,  1.57987849],
       [ 1.33748358,  1.29004108, -0.46697028],
       [-0.37373787, -0.22021339, -0.52941598],
       [ 0.74067882, -0.79556616, -0.39251251],
       [-0.75914684,  0.16646147,  2.22535186],
       [ 0.12008512, -0.96938058,  0.05000075],
       [-1.32889545,  0.31147169,  0.23795395],
       [ 0.2119362 ,  0.47765278, -0.0976922 ],
       [ 0.13551963,  0.44558949,  1.90940387],
       [ 0.87607052,  1.11719757,  0.62955101],
       [ 0.8630371 , -1.70701661,  0.47006564],
       [-0.97996414,  0.25728477,  0.89843618],
       [-1.42322307,  0.25964647, -0.65048082],
       [ 1.580251  , -0.3142048 ,  0.63919311],
       [ 1.95473317, -1.51552846,  0.1436534 ],
       [-0.7221338 ,  0.84588397, -0.29941785],
       [-0.44837652, -1.04596934,  0.24432642],
       [-0.0929797 , -1.08974158,  0.56177711],
       [ 2.82084974, -0.08072931,  0.77042241],
       [-1.48216309,  0.36591366,  1.35139741],
       [-0.3640665 , -0.18288453, -0.92213874],
       [-0.58940142,  0.59251817, -0.11977769],
       [ 0.70506868,  0.80862606,  2.05842293],
       [ 0.65980106,  1.85389319,  1.03040533],
       [ 0.36310659, -0.51209611,  0.16974761]])

In [ ]:

# 查看统计摘要
data.describe()

Out[ ]:

	A	B	C
count	30.000000	30.000000	30.000000
mean	0.140827	-0.138392	0.300276
std	1.009620	0.888840	1.014414
min	-1.482163	-1.901083	-2.492806
25%	-0.554145	-0.713123	-0.369239
50%	0.113086	-0.201549	0.241140
75%	0.731776	0.425671	0.882482
max	2.820850	1.853893	2.225352

In [ ]:

# 列数据
data[['A','B']]

Out[ ]:

	A	B
2021-01-31	-1.005821	-0.747159
2021-02-28	0.106087	-0.611014
2021-03-31	0.923487	-1.901083
2021-04-30	0.045023	-0.501125
2021-05-31	-0.015439	-0.328349
2021-06-30	0.366951	-0.421883
2021-07-31	1.337484	1.290041
2021-08-31	-0.373738	-0.220213
2021-09-30	0.740679	-0.795566
2021-10-31	-0.759147	0.166461
2021-11-30	0.120085	-0.969381
2021-12-31	-1.328895	0.311472
2022-01-31	0.211936	0.477653
2022-02-28	0.135520	0.445589
2022-03-31	0.876071	1.117198
2022-04-30	0.863037	-1.707017
2022-05-31	-0.979964	0.257285
2022-06-30	-1.423223	0.259646
2022-07-31	1.580251	-0.314205
2022-08-31	1.954733	-1.515528
2022-09-30	-0.722134	0.845884
2022-10-31	-0.448377	-1.045969
2022-11-30	-0.092980	-1.089742
2022-12-31	2.820850	-0.080729
2023-01-31	-1.482163	0.365914
2023-02-28	-0.364066	-0.182885
2023-03-31	-0.589401	0.592518
2023-04-30	0.705069	0.808626
2023-05-31	0.659801	1.853893
2023-06-30	0.363107	-0.512096

In [ ]:

# 行数据
data.iloc[0:10]

Out[ ]:

	A	B	C
2021-01-31	-1.005821	-0.747159	-0.590444
2021-02-28	0.106087	-0.611014	-2.492806
2021-03-31	0.923487	-1.901083	-1.139865
2021-04-30	0.045023	-0.501125	0.834619
2021-05-31	-0.015439	-0.328349	0.905197
2021-06-30	0.366951	-0.421883	1.579878
2021-07-31	1.337484	1.290041	-0.466970
2021-08-31	-0.373738	-0.220213	-0.529416
2021-09-30	0.740679	-0.795566	-0.392513
2021-10-31	-0.759147	0.166461	2.225352

In [ ]:

# 行列切片
data.loc['20210101':'20220101','A':'B']   # 20210101到20220101的A/B两列数据

Out[ ]:

	A	B
2021-01-31	-1.005821	-0.747159
2021-02-28	0.106087	-0.611014
2021-03-31	0.923487	-1.901083
2021-04-30	0.045023	-0.501125
2021-05-31	-0.015439	-0.328349
2021-06-30	0.366951	-0.421883
2021-07-31	1.337484	1.290041
2021-08-31	-0.373738	-0.220213
2021-09-30	0.740679	-0.795566
2021-10-31	-0.759147	0.166461
2021-11-30	0.120085	-0.969381
2021-12-31	-1.328895	0.311472

In [ ]:

# 按值筛选
## 小数取2位
data = round(data,2)
## A列中数值是0.74的那一行
data[data['A']==0.74]
## A列中数值是0.74的那一行的A列
data[data['A']==0.74]['A']

Out[ ]:

2021-09-30    0.74
Freq: M, Name: A, dtype: float64

In [ ]:

# 按条件筛选(布尔值)
data[data['A']>0.5]

Out[ ]:

	A	B	C
2021-03-31	0.92	-1.90	-1.14
2021-07-31	1.34	1.29	-0.47
2021-09-30	0.74	-0.80	-0.39
2022-03-31	0.88	1.12	0.63
2022-04-30	0.86	-1.71	0.47
2022-07-31	1.58	-0.31	0.64
2022-08-31	1.95	-1.52	0.14
2022-12-31	2.82	-0.08	0.77
2023-04-30	0.71	0.81	2.06
2023-05-31	0.66	1.85	1.03

In [ ]:

data[data>0.5]   # 不满足条件的那一个数值会变成nan

Out[ ]:

	A	B	C
2021-01-31	NaN	NaN	NaN
2021-02-28	NaN	NaN	NaN
2021-03-31	0.92	NaN	NaN
2021-04-30	NaN	NaN	0.83
2021-05-31	NaN	NaN	0.91
2021-06-30	NaN	NaN	1.58
2021-07-31	1.34	1.29	NaN
2021-08-31	NaN	NaN	NaN
2021-09-30	0.74	NaN	NaN
2021-10-31	NaN	NaN	2.23
2021-11-30	NaN	NaN	NaN
2021-12-31	NaN	NaN	NaN
2022-01-31	NaN	NaN	NaN
2022-02-28	NaN	NaN	1.91
2022-03-31	0.88	1.12	0.63
2022-04-30	0.86	NaN	NaN
2022-05-31	NaN	NaN	0.90
2022-06-30	NaN	NaN	NaN
2022-07-31	1.58	NaN	0.64
2022-08-31	1.95	NaN	NaN
2022-09-30	NaN	0.85	NaN
2022-10-31	NaN	NaN	NaN
2022-11-30	NaN	NaN	0.56
2022-12-31	2.82	NaN	0.77
2023-01-31	NaN	NaN	1.35
2023-02-28	NaN	NaN	NaN
2023-03-31	NaN	0.59	NaN
2023-04-30	0.71	0.81	2.06
2023-05-31	0.66	1.85	1.03
2023-06-30	NaN	NaN	NaN

In [ ]:

# 针对上面的结果去除NaN
data[data>0.5].dropna()

Out[ ]:

	A	B	C
2022-03-31	0.88	1.12	0.63
2023-04-30	0.71	0.81	2.06
2023-05-31	0.66	1.85	1.03

In [ ]:

# 去除重复值
data.drop_duplicates()

Out[ ]:

	A	B	C
2021-01-31	-1.01	-0.75	-0.59
2021-02-28	0.11	-0.61	-2.49
2021-03-31	0.92	-1.90	-1.14
2021-04-30	0.05	-0.50	0.83
2021-05-31	-0.02	-0.33	0.91
2021-06-30	0.37	-0.42	1.58
2021-07-31	1.34	1.29	-0.47
2021-08-31	-0.37	-0.22	-0.53
2021-09-30	0.74	-0.80	-0.39
2021-10-31	-0.76	0.17	2.23
2021-11-30	0.12	-0.97	0.05
2021-12-31	-1.33	0.31	0.24
2022-01-31	0.21	0.48	-0.10
2022-02-28	0.14	0.45	1.91
2022-03-31	0.88	1.12	0.63
2022-04-30	0.86	-1.71	0.47
2022-05-31	-0.98	0.26	0.90
2022-06-30	-1.42	0.26	-0.65
2022-07-31	1.58	-0.31	0.64
2022-08-31	1.95	-1.52	0.14
2022-09-30	-0.72	0.85	-0.30
2022-10-31	-0.45	-1.05	0.24
2022-11-30	-0.09	-1.09	0.56
2022-12-31	2.82	-0.08	0.77
2023-01-31	-1.48	0.37	1.35
2023-02-28	-0.36	-0.18	-0.92
2023-03-31	-0.59	0.59	-0.12
2023-04-30	0.71	0.81	2.06
2023-05-31	0.66	1.85	1.03
2023-06-30	0.36	-0.51	0.17

In [ ]:

# 转置
data.T

Out[ ]:

	2021-01-31	2021-02-28	2021-03-31	2021-04-30	2021-05-31	2021-06-30	2021-07-31	2021-08-31	2021-09-30	2021-10-31	...	2022-09-30	2022-10-31	2022-11-30	2022-12-31	2023-01-31	2023-02-28	2023-03-31	2023-04-30	2023-05-31	2023-06-30
A	-1.01	0.11	0.92	0.05	-0.02	0.37	1.34	-0.37	0.74	-0.76	...	-0.72	-0.45	-0.09	2.82	-1.48	-0.36	-0.59	0.71	0.66	0.36
B	-0.75	-0.61	-1.90	-0.50	-0.33	-0.42	1.29	-0.22	-0.80	0.17	...	0.85	-1.05	-1.09	-0.08	0.37	-0.18	0.59	0.81	1.85	-0.51
C	-0.59	-2.49	-1.14	0.83	0.91	1.58	-0.47	-0.53	-0.39	2.23	...	-0.30	0.24	0.56	0.77	1.35	-0.92	-0.12	2.06	1.03	0.17

3 rows × 30 columns

In [ ]:

# 排序
data.sort_values(by='A',ascending=False)  # A列降序

Out[ ]:

	A	B	C
2022-12-31	2.82	-0.08	0.77
2022-08-31	1.95	-1.52	0.14
2022-07-31	1.58	-0.31	0.64
2021-07-31	1.34	1.29	-0.47
2021-03-31	0.92	-1.90	-1.14
2022-03-31	0.88	1.12	0.63
2022-04-30	0.86	-1.71	0.47
2021-09-30	0.74	-0.80	-0.39
2023-04-30	0.71	0.81	2.06
2023-05-31	0.66	1.85	1.03
2021-06-30	0.37	-0.42	1.58
2023-06-30	0.36	-0.51	0.17
2022-01-31	0.21	0.48	-0.10
2022-02-28	0.14	0.45	1.91
2021-11-30	0.12	-0.97	0.05
2021-02-28	0.11	-0.61	-2.49
2021-04-30	0.05	-0.50	0.83
2021-05-31	-0.02	-0.33	0.91
2022-11-30	-0.09	-1.09	0.56
2023-02-28	-0.36	-0.18	-0.92
2021-08-31	-0.37	-0.22	-0.53
2022-10-31	-0.45	-1.05	0.24
2023-03-31	-0.59	0.59	-0.12
2022-09-30	-0.72	0.85	-0.30
2021-10-31	-0.76	0.17	2.23
2022-05-31	-0.98	0.26	0.90
2021-01-31	-1.01	-0.75	-0.59
2021-12-31	-1.33	0.31	0.24
2022-06-30	-1.42	0.26	-0.65
2023-01-31	-1.48	0.37	1.35

In [ ]:

data.sort_index(ascending=False)

Out[ ]:

	A	B	C
2023-06-30	0.36	-0.51	0.17
2023-05-31	0.66	1.85	1.03
2023-04-30	0.71	0.81	2.06
2023-03-31	-0.59	0.59	-0.12
2023-02-28	-0.36	-0.18	-0.92
2023-01-31	-1.48	0.37	1.35
2022-12-31	2.82	-0.08	0.77
2022-11-30	-0.09	-1.09	0.56
2022-10-31	-0.45	-1.05	0.24
2022-09-30	-0.72	0.85	-0.30
2022-08-31	1.95	-1.52	0.14
2022-07-31	1.58	-0.31	0.64
2022-06-30	-1.42	0.26	-0.65
2022-05-31	-0.98	0.26	0.90
2022-04-30	0.86	-1.71	0.47
2022-03-31	0.88	1.12	0.63
2022-02-28	0.14	0.45	1.91
2022-01-31	0.21	0.48	-0.10
2021-12-31	-1.33	0.31	0.24
2021-11-30	0.12	-0.97	0.05
2021-10-31	-0.76	0.17	2.23
2021-09-30	0.74	-0.80	-0.39
2021-08-31	-0.37	-0.22	-0.53
2021-07-31	1.34	1.29	-0.47
2021-06-30	0.37	-0.42	1.58
2021-05-31	-0.02	-0.33	0.91
2021-04-30	0.05	-0.50	0.83
2021-03-31	0.92	-1.90	-1.14
2021-02-28	0.11	-0.61	-2.49
2021-01-31	-1.01	-0.75	-0.59

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基本操作dataframe pandas pd

openharmony特性release 3.2

526互联

3.2-DataFrame基本操作

数据概要¶

数据查询¶

其他¶