您如何将 plot 月份和年份数据转换为 matplotlib 中的条形图？

Question

I have data like this that I want to plot by month and year using matplotlib.

df = pd.DataFrame({'date':['2018-10-01', '2018-10-05', '2018-10-20','2018-10-21','2018-12-06',
                            '2018-12-16', '2018-12-27', '2019-01-08','2019-01-10','2019-01-11',
                            '2019-01-12', '2019-01-13', '2019-01-25', '2019-02-01','2019-02-25', 
                            '2019-04-05','2019-05-05','2018-05-07','2019-05-09','2019-05-10'],
                  'counts':[10,5,6,1,2,
                            5,7,20,30,8,
                            9,1,10,12,50,
                            8,3,10,40,4]})

First, I converted the datetime format, and get the year and month from each date.

df['date'] = pd.to_datetime(df['date'])

df['year'] = df['date'].dt.year
df['month'] = df['date'].dt.month

Then, I tried to do groupby like this.

aggmonth = df.groupby(['year', 'month']).sum()

And I want to visualize it in a barchart or something like that. But as you notice above, there are missing months in between the data. I want those missing months to be filled with 0s. I don't know how to do that in a dataframe like this. Previously, I asked this question about filling missing dates in a period of data. where I converted the dates to period range in month-year format.

by_month = pd.to_datetime(df['date']).dt.to_period('M').value_counts().sort_index()
by_month.index = pd.PeriodIndex(by_month.index)

df_month = by_month.rename_axis('month').reset_index(name='counts')
df_month

idx = pd.period_range(df_month['month'].min(), df_month['month'].max(), freq='M')
s = df_month.set_index('month').reindex(idx, fill_value=0)
s

But when I tried to plot s using matplotlib, it returned an error. It turned out you cannot plot a period data using matplotlib.

So basically I got these two ideas in my head, but both are stuck, and I don't know which one I should keep pursuing to get the result I want.

What is the best way to do this? Thanks.

Answer 1

Convert the date column to pandas datetime series, then use groupby on monthly period and aggregate the data using sum , next use DataFrame.resample on the aggregated dataframe to resample using monthly frequency:

df['date'] = pd.to_datetime(df['date'])
df1 = df.groupby(df['date'].dt.to_period('M')).sum()
df1 = df1.resample('M').asfreq().fillna(0)

Plotting the data:

df1.plot(kind='bar')