importmatplotlib.pyplotasplt# 按月份分组并计算平均值monthly_avg=df.groupby(df['date'].dt.month)['value'].mean()# 绘制柱状图plt.figure(figsize=(12,6))monthly_avg.plot(kind='bar')plt.title('Monthly Average Values')plt.xlabel('Month')plt.ylabel('Average Value')
The .resample() method truly shines when it comes to downsampling, as it allows us to apply various aggregation methods to summarize our data. For example, let's calculate both the monthly average and quarterly median temperatures for Madrid using .resample(). df.mean_temp.resample('M').mea...
data_stats.head() Step 11. Find the average windspeed in January for each location. Treat January 1961 and January 1962 both as January. 这一题主要靠的是时间序列的操作 data.loc[data.index.month == 1].mean() Step 12. Downsample the record to a yearly frequency for each location. 下面几...
这里,我本来想使用query函数,但是不行,貌似是识别不出这个month属性 8. Downsample the record to a yearly frequency for each location. 这个类似于时间序列的重构了,我记得之前有写过,参考: pandas-时间序列重构-resample df2.resample('Y').mean() 这里不好的地方时,这个label用的是最后1天,我暂时还不知道...
Let's now try to print the average quarterly (every three months) values for the dataset. You can see from the offset list that Q is used for quarterly frequency. Execute the following script: apple_data.resample(rule='Q').mean() The output of the script above looks like this: In ...