import pandas as pd
import numpy as np
pd.DataFrame({"one":[1,2,3],'two':[4,5,6]})
onetwo014125236
pd.DataFrame({"one":[1,2,3],'two':[4,5,6]},index=['a','b','c'])
onetwoa14b25c36
pd.DataFrame({'one':pd.Series([1,2,3],index=['a','b','c']),'two':pd.Series([1,2,3,4],index=['d','c','a','b'])})
onetwoa1.03b2.04c3.02dNaN1
pd.read_csv('test.csv')
abc012312462369
df = pd.DataFrame({'one':pd.Series([1,2,3],index=['a','b','c']),'two':pd.Series([1,2,3,4],index=['d','c','a','b'])})
df
onetwoa1.03b2.04c3.02dNaN1
df.to_csv('test2.csv')
1、index、columns和vlues属性
df = pd.DataFrame({'one':pd.Series([1,2,3],index=['a','b','c']),'two':pd.Series([1,2,3,4],index=['d','c','a','b'])})
df
onetwoa1.03b2.04c3.02dNaN1
df.index
Index(['a', 'b', 'c', 'd'], dtype='object')
df.columns
Index(['one', 'two'], dtype='object')
df.values
array([[ 1., 3.],
[ 2., 4.],
[ 3., 2.],
[nan, 1.]])
2.T属性
df.T
abcdone1.02.03.0NaNtwo3.04.02.01.0
3.describe()方法
df.describe()
onetwocount3.04.000000mean2.02.500000std1.01.290994min1.01.00000025%1.51.75000050%2.02.50000075%2.53.250000max3.04.000000
count: 该列数据共有多少条
mean：该列数据平均值
std：该列数据标准差
min：该列数据最小值
25%：该列数据从小到大25%位置上的数
50%：该列数据中位数
75%：该列数据从小到大75%位置上的数
max：该列数据最大值
df = pd.DataFrame({'one':pd.Series([1,2,3],index=['a','b','c']),'two':pd.Series([1,2,3,4],index=['d','c','a','b'])})
onetwoa1.03b2.04c3.02dNaN1
eg：取第one行第a列的1.0
df['one']['a']
1.0
df.loc['a','one']
1.0
df['one']
a 1.0
b 2.0
c 3.0
d NaN
Name: one, dtype: float64
df['a']
c:\users\lenovo\appdata\local\programs\python\python37\lib\site-packages\pandas\core\frame.py in __getitem__(self, key)
2904 if self.columns.nlevels > 1:
2905 return self._getitem_multilevel(key)
-> 2906 indexer = self.columns.get_loc(key)
2907 if is_integer(indexer):
2908 indexer = [indexer]
c:\users\lenovo\appdata\local\programs\python\python37\lib\site-packages\pandas\core\indexes\base.py in get_loc(self, key, method, tolerance)
2895 return self._engine.get_loc(casted_key)
2896 except KeyError as err:
-> 2897 raise KeyError(key) from err
2898
2899 if tolerance is not None:
KeyError: 'a'
</module></ipython-input-34-9637ce7feee6>
df.loc['a',:]
one 1.0
two 3.0
Name: a, dtype: float64
df = pd.DataFrame({'one':pd.Series([1,2,3],index=['a','b','c']),'two':pd.Series([1,2,3,4],index=['d','c','a','b'])})
df
onetwoa1.03b2.04c3.02dNaN1
eg：
df.loc[['a','b'],'one':'two']
onetwoa1.03b2.04
df = pd.DataFrame({'one':pd.Series([1,2,3],index=['a','b','c']),'two':pd.Series([1,2,3,4],index=['d','c','a','b'])})
df
onetwoa1.03b2.04c3.02dNaN1
df2 = pd.DataFrame({'two':[1,2,3,4],'one':[5,6,7,8]},index = ['d','c','a','b'])
df2
twooned15c26a37b48
df + df2
onetwoa8.06b10.08c9.04dNaN2
df
onetwoa1.03b2.04c3.02dNaN1
df.fillna(0)
onetwoa1.03b2.04c3.02d0.01
df.dropna()
onetwoa1.03b2.04c3.02
df
onetwoa1.03b2.04c3.02dNaN1
df.loc['c','two'] = np.nan
df.loc['d','two'] = np.nan
df
onetwoa1.03.0b2.04.0c3.0NaNdNaNNaN
df.dropna(how = 'all')
onetwoa1.03.0b2.04.0c3.0NaN
df2
twooned15c26a37b48
df2.loc['a','two'] = np.nan
df2
twooned1.05c2.06aNaN7b4.08
df2.dropna(axis=1)
oned5c6a7b8
df2.isnull()
twoonedFalseFalsecFalseFalseaTrueFalsebFalseFalse
1.求平均值
df = df2
df
twooned1.05c2.06aNaN7b4.08
df.mean()
two 2.333333
one 6.500000
dtype: float64
df.mean(axis=1)
d 3.0
c 4.0
a 7.0
b 6.0
dtype: float64
2.按值排序 ： sort_values（）
df
twooned1.05c2.06aNaN7b4.08
df.sort_values(by = 'two')
twooned1.05c2.06b4.08aNaN7
df.sort_values(by = 'two',ascending=False)
twooneb4.08c2.06d1.05aNaN7
df.sort_values(by = 'd',ascending=False,axis=1)
onetwod51.0c62.0a7NaNb84.0
3.按列排序 ： sort_index（）
df
twooned1.05c2.06aNaN7b4.08
df.sort_index()
twooneaNaN7b4.08c2.06d1.05
df.sort_index(ascending = False)
twooned1.05c2.06b4.08aNaN7
df.sort_index(ascending = True , axis = 1)
onetwod51.0c62.0a7NaNb84.0
1.pandas时间对象处理
pd.to_datetime(["2021-01-10","2021/MAY/1"])
DatetimeIndex(['2021-01-10', '2021-05-01'], dtype='datetime64[ns]', freq=None)
2.pandas时间对象自动生成
start：开始时间
end：结束时间
periods：可以指定start不指定end改指定periods，指的是生成从start开始的periods天时间，同样的，可以指定end不指定start改指定periods
pd.date_range('2021-01-01','2021-03-01')
DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',
'2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',
'2021-01-09', '2021-01-10', '2021-01-11', '2021-01-12',
'2021-01-13', '2021-01-14', '2021-01-15', '2021-01-16',
'2021-01-17', '2021-01-18', '2021-01-19', '2021-01-20',
'2021-01-21', '2021-01-22', '2021-01-23', '2021-01-24',
'2021-01-25', '2021-01-26', '2021-01-27', '2021-01-28',
'2021-01-29', '2021-01-30', '2021-01-31', '2021-02-01',
'2021-02-02', '2021-02-03', '2021-02-04', '2021-02-05',
'2021-02-06', '2021-02-07', '2021-02-08', '2021-02-09',
'2021-02-10', '2021-02-11', '2021-02-12', '2021-02-13',
'2021-02-14', '2021-02-15', '2021-02-16', '2021-02-17',
'2021-02-18', '2021-02-19', '2021-02-20', '2021-02-21',
'2021-02-22', '2021-02-23', '2021-02-24', '2021-02-25',
'2021-02-26', '2021-02-27', '2021-02-28', '2021-03-01'],
dtype='datetime64[ns]', freq='D')
pd.date_range('2021-01-01',periods=60)
DatetimeIndex(['2021-01-01', '2021-01-02', '2021-01-03', '2021-01-04',
'2021-01-05', '2021-01-06', '2021-01-07', '2021-01-08',
'2021-01-09', '2021-01-10', '2021-01-11', '2021-01-12',
'2021-01-13', '2021-01-14', '2021-01-15', '2021-01-16',
'2021-01-17', '2021-01-18', '2021-01-19', '2021-01-20',
'2021-01-21', '2021-01-22', '2021-01-23', '2021-01-24',
'2021-01-25', '2021-01-26', '2021-01-27', '2021-01-28',
'2021-01-29', '2021-01-30', '2021-01-31', '2021-02-01',
'2021-02-02', '2021-02-03', '2021-02-04', '2021-02-05',
'2021-02-06', '2021-02-07', '2021-02-08', '2021-02-09',
'2021-02-10', '2021-02-11', '2021-02-12', '2021-02-13',
'2021-02-14', '2021-02-15', '2021-02-16', '2021-02-17',
'2021-02-18', '2021-02-19', '2021-02-20', '2021-02-21',
'2021-02-22', '2021-02-23', '2021-02-24', '2021-02-25',
'2021-02-26', '2021-02-27', '2021-02-28', '2021-03-01'],
dtype='datetime64[ns]', freq='D')
freq：指定生成的时间间隔单位，默认为D（天），此外，还有‘H’（小时），‘W’（周）等
W（周）分为，‘W-MON’表示从start开始输出每周周一，默认只输入W表示'W-SUN'
B：只输出工作日
pd.date_range('2021-01-01',periods=60,freq='H')
DatetimeIndex(['2021-01-01 00:00:00', '2021-01-01 01:00:00',
'2021-01-01 02:00:00', '2021-01-01 03:00:00',
'2021-01-01 04:00:00', '2021-01-01 05:00:00',
'2021-01-01 06:00:00', '2021-01-01 07:00:00',
'2021-01-01 08:00:00', '2021-01-01 09:00:00',
'2021-01-01 10:00:00', '2021-01-01 11:00:00',
'2021-01-01 12:00:00', '2021-01-01 13:00:00',
'2021-01-01 14:00:00', '2021-01-01 15:00:00',
'2021-01-01 16:00:00', '2021-01-01 17:00:00',
'2021-01-01 18:00:00', '2021-01-01 19:00:00',
'2021-01-01 20:00:00', '2021-01-01 21:00:00',
'2021-01-01 22:00:00', '2021-01-01 23:00:00',
'2021-01-02 00:00:00', '2021-01-02 01:00:00',
'2021-01-02 02:00:00', '2021-01-02 03:00:00',
'2021-01-02 04:00:00', '2021-01-02 05:00:00',
'2021-01-02 06:00:00', '2021-01-02 07:00:00',
'2021-01-02 08:00:00', '2021-01-02 09:00:00',
'2021-01-02 10:00:00', '2021-01-02 11:00:00',
'2021-01-02 12:00:00', '2021-01-02 13:00:00',
'2021-01-02 14:00:00', '2021-01-02 15:00:00',
'2021-01-02 16:00:00', '2021-01-02 17:00:00',
'2021-01-02 18:00:00', '2021-01-02 19:00:00',
'2021-01-02 20:00:00', '2021-01-02 21:00:00',
'2021-01-02 22:00:00', '2021-01-02 23:00:00',
'2021-01-03 00:00:00', '2021-01-03 01:00:00',
'2021-01-03 02:00:00', '2021-01-03 03:00:00',
'2021-01-03 04:00:00', '2021-01-03 05:00:00',
'2021-01-03 06:00:00', '2021-01-03 07:00:00',
'2021-01-03 08:00:00', '2021-01-03 09:00:00',
'2021-01-03 10:00:00', '2021-01-03 11:00:00'],
dtype='datetime64[ns]', freq='H')
pd.date_range('2021-01-01',periods=60,freq='W')
DatetimeIndex(['2021-01-03', '2021-01-10', '2021-01-17', '2021-01-24',
'2021-01-31', '2021-02-07', '2021-02-14', '2021-02-21',
'2021-02-28', '2021-03-07', '2021-03-14', '2021-03-21',
'2021-03-28', '2021-04-04', '2021-04-11', '2021-04-18',
'2021-04-25', '2021-05-02', '2021-05-09', '2021-05-16',
'2021-05-23', '2021-05-30', '2021-06-06', '2021-06-13',
'2021-06-20', '2021-06-27', '2021-07-04', '2021-07-11',
'2021-07-18', '2021-07-25', '2021-08-01', '2021-08-08',
'2021-08-15', '2021-08-22', '2021-08-29', '2021-09-05',
'2021-09-12', '2021-09-19', '2021-09-26', '2021-10-03',
'2021-10-10', '2021-10-17', '2021-10-24', '2021-10-31',
'2021-11-07', '2021-11-14', '2021-11-21', '2021-11-28',
'2021-12-05', '2021-12-12', '2021-12-19', '2021-12-26',
'2022-01-02', '2022-01-09', '2022-01-16', '2022-01-23',
'2022-01-30', '2022-02-06', '2022-02-13', '2022-02-20'],
dtype='datetime64[ns]', freq='W-SUN')
pd.date_range('2021-01-01',periods=60,freq='W-Fri')
DatetimeIndex(['2021-01-01', '2021-01-08', '2021-01-15', '2021-01-22',
'2021-01-29', '2021-02-05', '2021-02-12', '2021-02-19',
'2021-02-26', '2021-03-05', '2021-03-12', '2021-03-19',
'2021-03-26', '2021-04-02', '2021-04-09', '2021-04-16',
'2021-04-23', '2021-04-30', '2021-05-07', '2021-05-14',
'2021-05-21', '2021-05-28', '2021-06-04', '2021-06-11',
'2021-06-18', '2021-06-25', '2021-07-02', '2021-07-09',
'2021-07-16', '2021-07-23', '2021-07-30', '2021-08-06',
'2021-08-13', '2021-08-20', '2021-08-27', '2021-09-03',
'2021-09-10', '2021-09-17', '2021-09-24', '2021-10-01',
'2021-10-08', '2021-10-15', '2021-10-22', '2021-10-29',
'2021-11-05', '2021-11-12', '2021-11-19', '2021-11-26',
'2021-12-03', '2021-12-10', '2021-12-17', '2021-12-24',
'2021-12-31', '2022-01-07', '2022-01-14', '2022-01-21',
'2022-01-28', '2022-02-04', '2022-02-11', '2022-02-18'],
dtype='datetime64[ns]', freq='W-FRI')
pd.date_range('2021-01-01',periods=60,freq='B')
DatetimeIndex(['2021-01-01', '2021-01-04', '2021-01-05', '2021-01-06',
'2021-01-07', '2021-01-08', '2021-01-11', '2021-01-12',
'2021-01-13', '2021-01-14', '2021-01-15', '2021-01-18',
'2021-01-19', '2021-01-20', '2021-01-21', '2021-01-22',
'2021-01-25', '2021-01-26', '2021-01-27', '2021-01-28',
'2021-01-29', '2021-02-01', '2021-02-02', '2021-02-03',
'2021-02-04', '2021-02-05', '2021-02-08', '2021-02-09',
'2021-02-10', '2021-02-11', '2021-02-12', '2021-02-15',
'2021-02-16', '2021-02-17', '2021-02-18', '2021-02-19',
'2021-02-22', '2021-02-23', '2021-02-24', '2021-02-25',
'2021-02-26', '2021-03-01', '2021-03-02', '2021-03-03',
'2021-03-04', '2021-03-05', '2021-03-08', '2021-03-09',
'2021-03-10', '2021-03-11', '2021-03-12', '2021-03-15',
'2021-03-16', '2021-03-17', '2021-03-18', '2021-03-19',
'2021-03-22', '2021-03-23', '2021-03-24', '2021-03-25'],
dtype='datetime64[ns]', freq='B')
pd.date_range('2021-01-01',periods=60,freq='1h20min')
DatetimeIndex(['2021-01-01 00:00:00', '2021-01-01 01:20:00',
'2021-01-01 02:40:00', '2021-01-01 04:00:00',
'2021-01-01 05:20:00', '2021-01-01 06:40:00',
'2021-01-01 08:00:00', '2021-01-01 09:20:00',
'2021-01-01 10:40:00', '2021-01-01 12:00:00',
'2021-01-01 13:20:00', '2021-01-01 14:40:00',
'2021-01-01 16:00:00', '2021-01-01 17:20:00',
'2021-01-01 18:40:00', '2021-01-01 20:00:00',
'2021-01-01 21:20:00', '2021-01-01 22:40:00',
'2021-01-02 00:00:00', '2021-01-02 01:20:00',
'2021-01-02 02:40:00', '2021-01-02 04:00:00',
'2021-01-02 05:20:00', '2021-01-02 06:40:00',
'2021-01-02 08:00:00', '2021-01-02 09:20:00',
'2021-01-02 10:40:00', '2021-01-02 12:00:00',
'2021-01-02 13:20:00', '2021-01-02 14:40:00',
'2021-01-02 16:00:00', '2021-01-02 17:20:00',
'2021-01-02 18:40:00', '2021-01-02 20:00:00',
'2021-01-02 21:20:00', '2021-01-02 22:40:00',
'2021-01-03 00:00:00', '2021-01-03 01:20:00',
'2021-01-03 02:40:00', '2021-01-03 04:00:00',
'2021-01-03 05:20:00', '2021-01-03 06:40:00',
'2021-01-03 08:00:00', '2021-01-03 09:20:00',
'2021-01-03 10:40:00', '2021-01-03 12:00:00',
'2021-01-03 13:20:00', '2021-01-03 14:40:00',
'2021-01-03 16:00:00', '2021-01-03 17:20:00',
'2021-01-03 18:40:00', '2021-01-03 20:00:00',
'2021-01-03 21:20:00', '2021-01-03 22:40:00',
'2021-01-04 00:00:00', '2021-01-04 01:20:00',
'2021-01-04 02:40:00', '2021-01-04 04:00:00',
'2021-01-04 05:20:00', '2021-01-04 06:40:00'],
dtype='datetime64[ns]', freq='80T')
3.时间序列
sr = pd.Series(np.arange(1000),index=pd.date_range('2020-01-01',periods=1000))
sr
2020-01-01 0
2020-01-02 1
2020-01-03 2
2020-01-04 3
2020-01-05 4
...
2022-09-22 995
2022-09-23 996
2022-09-24 997
2022-09-25 998
2022-09-26 999
Freq: D, Length: 1000, dtype: int32
sr.index
DatetimeIndex(['2020-01-01', '2020-01-02', '2020-01-03', '2020-01-04',
'2020-01-05', '2020-01-06', '2020-01-07', '2020-01-08',
'2020-01-09', '2020-01-10',
...
'2022-09-17', '2022-09-18', '2022-09-19', '2022-09-20',
'2022-09-21', '2022-09-22', '2022-09-23', '2022-09-24',
'2022-09-25', '2022-09-26'],
dtype='datetime64[ns]', length=1000, freq='D')
sr['2020']
2020-01-01 0
2020-01-02 1
2020-01-03 2
2020-01-04 3
2020-01-05 4
...
2020-12-27 361
2020-12-28 362
2020-12-29 363
2020-12-30 364
2020-12-31 365
Freq: D, Length: 366, dtype: int32
sr['2020-3']
2020-03-01 60
2020-03-02 61
2020-03-03 62
2020-03-04 63
2020-03-05 64
2020-03-06 65
2020-03-07 66
2020-03-08 67
2020-03-09 68
2020-03-10 69
2020-03-11 70
2020-03-12 71
2020-03-13 72
2020-03-14 73
2020-03-15 74
2020-03-16 75
2020-03-17 76
2020-03-18 77
2020-03-19 78
2020-03-20 79
2020-03-21 80
2020-03-22 81
2020-03-23 82
2020-03-24 83
2020-03-25 84
2020-03-26 85
2020-03-27 86
2020-03-28 87
2020-03-29 88
2020-03-30 89
2020-03-31 90
Freq: D, dtype: int32
sr['2020-3-19']
78
sr['2020-03':'2021-05-1']
2020-03-01 60
2020-03-02 61
2020-03-03 62
2020-03-04 63
2020-03-05 64
...
2021-04-27 482
2021-04-28 483
2021-04-29 484
2021-04-30 485
2021-05-01 486
Freq: D, Length: 427, dtype: int32
sr
2020-01-01 0
2020-01-02 1
2020-01-03 2
2020-01-04 3
2020-01-05 4
...
2022-09-22 995
2022-09-23 996
2022-09-24 997
2022-09-25 998
2022-09-26 999
Freq: D, Length: 1000, dtype: int32
sr.resample('W').sum()
2020-01-05 10
2020-01-12 56
2020-01-19 105
2020-01-26 154
2020-02-02 203
...
2022-09-04 6818
2022-09-11 6867
2022-09-18 6916
2022-09-25 6965
2022-10-02 999
Freq: W-SUN, Length: 144, dtype: int32
sr.resample('W').mean()
2020-01-05 2
2020-01-12 8
2020-01-19 15
2020-01-26 22
2020-02-02 29
...
2022-09-04 974
2022-09-11 981
2022-09-18 988
2022-09-25 995
2022-10-02 999
Freq: W-SUN, Length: 144, dtype: int32
pd.read_csv('maotai.csv')
日期收盘开盘高低交易量涨跌幅02021/11/121,773.781,778.001,785.051,767.001.76M0.24%12021/11/111,769.601,752.931,769.601,741.502.27M0.89%22021/11/101,753.991,790.011,795.001,735.003.53M-2.01%32021/11/91,790.011,819.981,827.871,782.002.74M-1.65%42021/11/81,820.101,820.001,830.801,802.051.77M0.01%……………………2392020/11/181,693.651,715.001,720.531,683.163.52M-1.29%2402020/11/171,715.801,740.001,742.351,701.072.52M-0.82%2412020/11/161,730.051,711.001,730.051,697.263.06M1.47%2422020/11/131,705.001,724.001,728.881,691.002.82M-1.72%2432020/11/121,734.791,730.011,750.001,722.272.35M0.20%
244 rows × 7 columns
df = pd.read_csv('maotai.csv',index_col=0)
df
收盘开盘高低交易量涨跌幅日期2021/11/121,773.781,778.001,785.051,767.001.76M0.24%2021/11/111,769.601,752.931,769.601,741.502.27M0.89%2021/11/101,753.991,790.011,795.001,735.003.53M-2.01%2021/11/91,790.011,819.981,827.871,782.002.74M-1.65%2021/11/81,820.101,820.001,830.801,802.051.77M0.01%…………………2020/11/181,693.651,715.001,720.531,683.163.52M-1.29%2020/11/171,715.801,740.001,742.351,701.072.52M-0.82%2020/11/161,730.051,711.001,730.051,697.263.06M1.47%2020/11/131,705.001,724.001,728.881,691.002.82M-1.72%2020/11/121,734.791,730.011,750.001,722.272.35M0.20%
244 rows × 6 columns
df.index
Index(['2021/11/12', '2021/11/11', '2021/11/10', '2021/11/9', '2021/11/8',
'2021/11/5', '2021/11/4', '2021/11/3', '2021/11/2', '2021/11/1',
...
'2020/11/25', '2020/11/24', '2020/11/23', '2020/11/20', '2020/11/19',
'2020/11/18', '2020/11/17', '2020/11/16', '2020/11/13', '2020/11/12'],
dtype='object', name='日期', length=244)
df = pd.read_csv('maotai.csv',index_col=0,thousands=',',parse_dates=True)
df
收盘开盘高低交易量涨跌幅日期2021-11-121773.781778.001785.051767.001.76M0.24%2021-11-111769.601752.931769.601741.502.27M0.89%2021-11-101753.991790.011795.001735.003.53M-2.01%2021-11-091790.011819.981827.871782.002.74M-1.65%2021-11-081820.101820.001830.801802.051.77M0.01%…………………2020-11-181693.651715.001720.531683.163.52M-1.29%2020-11-171715.801740.001742.351701.072.52M-0.82%2020-11-161730.051711.001730.051697.263.06M1.47%2020-11-131705.001724.001728.881691.002.82M-1.72%2020-11-121734.791730.011750.001722.272.35M0.20%
244 rows × 6 columns
df.index
DatetimeIndex(['2021-11-12', '2021-11-11', '2021-11-10', '2021-11-09',
'2021-11-08', '2021-11-05', '2021-11-04', '2021-11-03',
'2021-11-02', '2021-11-01',
...
'2020-11-25', '2020-11-24', '2020-11-23', '2020-11-20',
'2020-11-19', '2020-11-18', '2020-11-17', '2020-11-16',
'2020-11-13', '2020-11-12'],
dtype='datetime64[ns]', name='日期', length=244, freq=None)
df = pd.read_csv('maotai.csv',index_col=0,parse_dates=[0])
df.index
DatetimeIndex(['2021-11-12', '2021-11-11', '2021-11-10', '2021-11-09',
'2021-11-08', '2021-11-05', '2021-11-04', '2021-11-03',
'2021-11-02', '2021-11-01',
...
'2020-11-25', '2020-11-24', '2020-11-23', '2020-11-20',
'2020-11-19', '2020-11-18', '2020-11-17', '2020-11-16',
'2020-11-13', '2020-11-12'],
dtype='datetime64[ns]', name='日期', length=244, freq=None)
pd.read_csv('maotai.csv',index_col=0,header=None)
1234560日期收盘开盘高低交易量涨跌幅2021/11/121,773.781,778.001,785.051,767.001.76M0.24%2021/11/111,769.601,752.931,769.601,741.502.27M0.89%2021/11/101,753.991,790.011,795.001,735.003.53M-2.01%2021/11/91,790.011,819.981,827.871,782.002.74M-1.65%…………………2020/11/181,693.651,715.001,720.531,683.163.52M-1.29%2020/11/171,715.801,740.001,742.351,701.072.52M-0.82%2020/11/161,730.051,711.001,730.051,697.263.06M1.47%2020/11/131,705.001,724.001,728.881,691.002.82M-1.72%2020/11/121,734.791,730.011,750.001,722.272.35M0.20%
245 rows × 6 columns
pd.read_csv('maotai.csv',index_col=0,header=None,names=['a','b','c','d','e','f','g'])
bcdefga日期收盘开盘高低交易量涨跌幅2021/11/121,773.781,778.001,785.051,767.001.76M0.24%2021/11/111,769.601,752.931,769.601,741.502.27M0.89%2021/11/101,753.991,790.011,795.001,735.003.53M-2.01%2021/11/91,790.011,819.981,827.871,782.002.74M-1.65%…………………2020/11/181,693.651,715.001,720.531,683.163.52M-1.29%2020/11/171,715.801,740.001,742.351,701.072.52M-0.82%2020/11/161,730.051,711.001,730.051,697.263.06M1.47%2020/11/131,705.001,724.001,728.881,691.002.82M-1.72%2020/11/121,734.791,730.011,750.001,722.272.35M0.20%
245 rows × 6 columns
pd.read_csv('maotai.csv',index_col=0,header=None,names=['a','b','c','d','e','f','g'],na_values=['收盘','开盘'])
bcdefga日期NaNNaN高低交易量涨跌幅2021/11/121,773.781,778.001,785.051,767.001.76M0.24%2021/11/111,769.601,752.931,769.601,741.502.27M0.89%2021/11/101,753.991,790.011,795.001,735.003.53M-2.01%2021/11/91,790.011,819.981,827.871,782.002.74M-1.65%…………………2020/11/181,693.651,715.001,720.531,683.163.52M-1.29%2020/11/171,715.801,740.001,742.351,701.072.52M-0.82%2020/11/161,730.051,711.001,730.051,697.263.06M1.47%2020/11/131,705.001,724.001,728.881,691.002.82M-1.72%2020/11/121,734.791,730.011,750.001,722.272.35M0.20%
245 rows × 6 columns
Original: https://blog.csdn.net/qq_49259434/article/details/121310751
Author: 这个人不主动
Title: 成为华尔街金融巨鳄第三课： Pandas2:学会使用Pandas-DataFrame
原创文章受到原创版权保护。转载请注明出处：https://www.johngo689.com/738664/
转载文章受原作者版权保护。转载请注明原作者出处！