title: Why is matplotlib .plot(kind='bar') plot so different to .plot()
body: <p>This may be a very stupid question, but when plotting a Pandas DataFrame using .plot() it is very quick and produces a graph with an appropriate index. As soon as I try to change this to a bar chart, it just seems to lose all formatting and the index goes wild. Why is this the case? And is there an easy way to just plot a bar chart with the same format as the line chart?</p>
  
  <pre><code>import matplotlib.pyplot as plt
  import numpy as np
  import pandas as pd
  
  df = pd.DataFrame()
  df['Date'] = pd.date_range(start='01/01/2012', end='31/12/2018')
  df['Value'] = np.random.randint(low=5, high=100, size=len(df))
  df.set_index('Date', inplace=True)
  
  df.plot()
  plt.show()
  
  df.plot(kind='bar')
  plt.show()
  </code></pre>
  
  <p><a href="https://i.stack.imgur.com/5ITl0.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/5ITl0.png" alt="df.plot()"></a></p>
  
  <p><a href="https://i.stack.imgur.com/OcNB5.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/OcNB5.png" alt="df.plot(kind=&#39;bar&#39;"></a></p>
  
  <p>Update:
  For comparison, if I take the data and put it into Excel, then create a line plot and a bar ('column') plot it instantly will convert the plot and keep the axis labels as they were for the line plot. If I try to produce many (thousands) of bar charts in Python with years of daily data, this takes a long time. Is there just an equivalent way of doing this Excel transformation in Python? </p>
  
  <p><a href="https://i.stack.imgur.com/rZ0ng.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/rZ0ng.png" alt="Excel plots"></a></p>