Pandas bar plot changes date format

Question

I have a simple stacked line plot that has exactly the date format I want magically set when using the following code.

df_ts = df.resample("W", how='max') df_ts.plot(figsize=(12,8), stacked=True) 

However, the dates mysteriously transform themselves to an ugly and unreadable format when plotting the same data as a bar plot.

df_ts = df.resample("W", how='max') df_ts.plot(kind='bar', figsize=(12,8), stacked=True) 

The original data was transformed a bit to have the weekly max. Why is this radical change in automatically set dates happening? How can I have the nicely formatted dates as above?

Here is some dummy data

start = pd.to_datetime("1-1-2012") idx = pd.date_range(start, periods= 365).tolist() df=pd.DataFrame({'A':np.random.random(365), 'B':np.random.random(365)}) df.index = idx df_ts = df.resample('W', how= 'max') df_ts.plot(kind='bar', stacked=True) 

Answer 1

The plotting code assumes that each bar in a bar plot deserves its own label. You could override this assumption by specifying your own formatter:

ax.xaxis.set_major_formatter(formatter) 

The pandas.tseries.converter.TimeSeries_DateFormatter that Pandas uses to format the dates in the "good" plot works well with line plots when the x-values are dates. However, with a bar plot the x-values (at least those received by TimeSeries_DateFormatter.__call__) are merely integers starting at zero. If you try to use TimeSeries_DateFormatter with a bar plot, all the labels thus start at the Epoch, 1970-1-1 UTC, since this is the date which corresponds to zero. So the formatter used for line plots is unfortunately useless for bar plots (at least as far as I can see).

The easiest way I see to produce the desired formatting is to generate and set the labels explicitly:

import numpy as np import matplotlib.pyplot as plt import pandas as pd import matplotlib.ticker as ticker  start = pd.to_datetime("5-1-2012") idx = pd.date_range(start, periods= 365) df = pd.DataFrame({'A':np.random.random(365), 'B':np.random.random(365)}) df.index = idx df_ts = df.resample('W', how= 'max')  ax = df_ts.plot(kind='bar', x=df_ts.index, stacked=True)  # Make most of the ticklabels empty so the labels don't get too crowded ticklabels = ['']*len(df_ts.index) # Every 4th ticklable shows the month and day ticklabels[::4] = [item.strftime('%b %d') for item in df_ts.index[::4]] # Every 12th ticklabel includes the year ticklabels[::12] = [item.strftime('%b %d\n%Y') for item in df_ts.index[::12]] ax.xaxis.set_major_formatter(ticker.FixedFormatter(ticklabels)) plt.gcf().autofmt_xdate()  plt.show() 

yields

---

For those looking for a simple example of a bar plot with dates:

import numpy as np import pandas as pd import matplotlib.pyplot as plt import matplotlib.ticker as mticker  dates = pd.date_range('2012-1-1', '2017-1-1', freq='M') df = pd.DataFrame({'A':np.random.random(len(dates)), 'Date':dates}) fig, ax = plt.subplots() df.plot.bar(x='Date', y='A', ax=ax) ticklabels = ['']*len(df) skip = len(df)//12 ticklabels[::skip] = df['Date'].iloc[::skip].dt.strftime('%Y-%m-%d') ax.xaxis.set_major_formatter(mticker.FixedFormatter(ticklabels)) fig.autofmt_xdate()  # fixes the tracker # https://matplotlib.org/users/recipes.html def fmt(x, pos=0, max_i=len(ticklabels)-1):     i = int(x)      i = 0 if i < 0 else max_i if i > max_i else i     return dates[i] ax.fmt_xdata = fmt plt.show() 

Answer 2

I've struggled with this problem too, and after reading several posts came up with the following solution, which seems to me slightly clearer than matplotlib.dates approach.

Labels without modification:

# Use DatetimeIndex instead of date_range for pandas earlier than 1.0.0 version  timeline = pd.date_range(start='2018, November', freq='M', periods=15) df = pd.DataFrame({'date': timeline, 'value': np.random.randn(15)}) df.set_index('date', inplace=True) df.plot(kind='bar', figsize=(12, 8), color='#2ecc71') 

Labels with modification:

def line_format(label):     """     Convert time label to the format of pandas line plot     """     month = label.month_name()[:3]     if month == 'Jan':         month += f'\n{label.year}'     return month  # Note that we specify rot here ax = df.plot(kind='bar', figsize=(12, 8), color='#2ecc71', rot=0) ax.set_xticklabels(map(line_format, df.index)) 

This approach will add year to the label only if it is January