Pandas 教程-Pandas日期时间

整理：python架构师

1. DatetimeIndex

2. 日期范围生成

3. 本地化和转换

4. 时区处理

5. 时间序列操作

时间序列工具对于数据科学应用程序非常有用，并与Python中使用的其他包一起使用。

示例1: DatetimeIndex

下面的代码生成从'5/4/2013'开始的八个日期，频率为一秒。

# Python Program for Pandas datetime Library  import pandas as pd       # Create the dates with frequency       info = pd.date_range('5/4/2013', periods = 8, freq ='S')       # Display the generated sequence of dates    info   

DatetimeIndex(['2013-05-04 00:00:00', '2013-05-04 00:00:01',               '2013-05-04 00:00:02', '2013-05-04 00:00:03',               '2013-05-04 00:00:04', '2013-05-04 00:00:05',               '2013-05-04 00:00:06', '2013-05-04 00:00:07'],              dtype='datetime64[ns]', freq='S')

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示例2: 转换

下面的代码使用Pandas中的pd.to_datetime()函数，将具有年、月和日的单独列的DataFrame转换为单个日期时间格式。

# Python Program for Pandas datetime Library  import pandas as pd  # Create a DataFrame with 'year', 'month', and 'day' columns  info = pd.DataFrame({'year': [2014, 2012],    'month': [5, 7],    'day': [20, 17]})    # Convert the DataFrame columns to datetime format  pd.to_datetime(info)

0   2014-05-201   2012-07-17dtype: datetime64[ns]

示例3: 生成范围

下面的代码生成从'2017-06-04'开始的五个日期，频率为一秒。

# Python Program for Pandas datetime Library  import pandas as pd    start_date = '2017-06-04'  num_periods = 5  frequency = 'S'  dates = pd.date_range(start=start_date, periods=num_periods, freq=frequency)  # Display the generated sequence of dates    dates

DatetimeIndex(['2017-06-04 00:00:00',                '2017-06-04 00:00:01',               '2017-06-04 00:00:02',               '2017-06-04 00:00:03',               '2017-06-04 00:00:04'],               dtype='datetime64[ns]', freq='S')

示例4: 本地化

下面的Python日期时间代码将由变量'dmy'表示的日期时间序列的时区本地化为UTC，使用Pandas中的tz_localize()函数。

# Python Program for Pandas datetime Library  import pandas as pd    # Localize the timezone of the 'dmy' datetime sequence to UTC  info = dmy.tz_localize('UTC')     # Display the updated datetime sequence  info      

DatetimeIndex(['2017-06-04 00:00:00+00:00', '2017-06-04 00:00:01+00:00',               '2017-06-04 00:00:02+00:00',                '2017-06-04 00:00:03+00:00',               '2017-06-04 00:00:04+00:00'],              dtype='datetime64[ns, UTC]', freq='S')

示例5: 时间序列操作

下面的代码用于执行时间序列操作，如滚动均值。

# Python Program for Pandas datetime Library  import pandas as pd    # Create a sample DataFrame with datetime index  data = {'Value': [10, 15, 8, 12, 9]}  index = pd.date_range('2022-01-01', periods=5, freq='D')  df = pd.DataFrame(data, index=index)  # Perform rolling window calculation on the 'Value' column  rolling_mean = df['Value'].rolling(window=3).mean()  # Print the DataFrame and rolling mean values  print("DataFrame:")  print(df)  print("Rolling Mean:")  print(rolling_mean)

DataFrame:            Value2022-01-01     102022-01-02     152022-01-03      82022-01-04     122022-01-05      9
Rolling Mean:2022-01-01          NaN2022-01-02          NaN2022-01-03     11.0000002022-01-04     11.6666672022-01-05      9.666667Freq: D, Name: Value, dtype: float64

结论:

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