Python将年中的某天转换为轴上的月份

Question

I have a time series that I would like to plot year on year. I want the data to be daily, but the axis to show each month as "Jan", "Feb" etc.

At the moment I can get the daily data, BUT the axis is 1-366 (the day of the year).

Or I can get the monthly axis as 1, 2, 3 etc (by changing the index to df.index.month), BUT then the data is monthly.

How can I convert the day of year axis into months? Or how can I do this?

Code showing the daily data, but the axis is wrong:

# import
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

# create fake time series dataframe
index = pd.date_range(start='01-Jan-2012', end='31-12-2018', freq='D')
data = np.random.randn(len(index))
df = pd.DataFrame(data, index, columns=['Data'])

# pivot to get by day in rows, then year in columns
df_pivot = pd.pivot_table(df, index=df.index.dayofyear, columns=df.index.year, values='Data')
df_pivot.plot()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()

Answer 1

This can be done using the xticks function . Simply add the following code before plt.show() :

plt.xticks(np.linspace(0,365,13)[:-1], ('Jan', 'Feb' ... 'Nov', 'Dec'))

Or the following to have the month names appear in the middle of the month:

plt.xticks(np.linspace(15,380,13)[:-1], ('Jan', 'Feb' ... 'Nov', 'Dec'))

Answer 2

It may be more straightforward to simply add a datetime index to your pivoted dataframe.

df_pivot.index = pd.date_range(
    df.index.max() - pd.Timedelta(days=df_pivot.shape[0]),
    freq='D', periods=df_pivot.shape[0])

df_pivot.plot()
plt.legend(loc='center left', bbox_to_anchor=(1, 0.5))
plt.show()

The resulting plot has the axis as desired:

This method also has the advantage over the accepted answer of working irrespective of your start and end date. For example, if you change your index 's end date to end='30-Jun-2018' , the axis adapts nicely to fit the data: