Downsampling: Decreasing the frequency (e.g., from monthly to yearly). This involves aggregating data points within the new, larger time intervals. Resampling Using Pandas asfreq() Method We can perform resampl
可以使用pandas的to_datetime函数将列转换为日期时间类型,例如: 代码语言:txt 复制 df['date_column'] = pd.to_datetime(df['date_column']) 接下来,可以使用resample函数将日期时间列按照指定的时间间隔进行重采样,例如按月、季度、年等进行分组。以下是一些常用的时间间隔: 'D':按天 'W':按周 'M':按...
data.groupby(data.index.to_period('W')).mean() Step 15. Calculate the min, max and mean windspeeds and standard deviations of the windspeeds across all locations for each week (assume that the first week starts on January 2 1961) for the first 52 weeks. weekly = data.resample('W')....
__init__(self, '//blp/refdata', ignore_security_error=ignore_security_error, ignore_field_error=ignore_field_error) period = period or 'DAILY' assert period in ('DAILY', 'WEEKLY', 'MONTHLY', 'QUARTERLY', 'SEMI-ANNUAL', 'YEARLY') self.is_single_sid = is_single_sid = isinstance(...
apple_data.resample(rule='Q').mean() The output of the script above looks like this: In addition to finding the aggregated values for all the columns in the dataset. You can also re-sample data for a particular column. Let's plot the bar plot that displays the yearly mean value for...