content: import numpy as np
  import pandas
  import matplotlib.pyplot as plt
  
  def entries_histogram(turnstile_weather):
      '''
      Before we perform any analysis, it might be useful to take a
      look at the data we're hoping to analyze. More specifically, let's 
      examine the hourly entries in our NYC subway data and determine what
      distribution the data follows. This data is stored in a dataframe
      called turnstile_weather under the ['ENTRIESn_hourly'] column.
      
      Let's plot two histograms on the same axes to show hourly
      entries when raining vs. when not raining. Here's an example on how
      to plot histograms with pandas and matplotlib:
      turnstile_weather['column_to_graph'].hist()
      
      Your histogram may look similar to bar graph in the instructor notes below.
      
      You can read a bit about using matplotlib and pandas to plot histograms here:
      http://pandas.pydata.org/pandas-docs/stable/visualization.html#histograms
      
      You can see the information contained within the turnstile weather data here:
      https://www.dropbox.com/s/meyki2wl9xfa7yk/turnstile_data_master_with_weather.csv
      '''
      
      plt.figure()
      #print turnstile_weather['rain'] == 1
      turnstile_weather[turnstile_weather['rain' ]== 0]['ENTRIESn_hourly'].hist()  # your code here to plot a historgram for hourly entries when it is raining
      turnstile_weather[turnstile_weather['rain'] == 1]['ENTRIESn_hourly'].hist() # your code here to plot a historgram for hourly entries when it is not raining
      return plt
  