title: Pandas split data frame into multiple csv's based on column value
body: <p>I have a question very <a href="https://stackoverflow.com/questions/36192633/python-pandas-split-a-data-frame-based-on-a-column-value">similar to this one</a> but I need to take it a step further by saving split data frames to csv.</p>
  
  <pre><code>import pandas as pd
  import numpy as np
  import os
  
  df = pd.DataFrame({ 'CITY' : np.random.choice(['PHOENIX','ATLANTA','CHICAGO', 'MIAMI', 'DENVER'], 1000),
                      'DAY': np.random.choice(['Monday','Tuesday','Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday'], 1000),
                      'TIME_BIN': np.random.randint(1, 86400, size=1000),
                      'COUNT': np.random.randint(1, 700, size=1000)})
  
  df['TIME_BIN'] = pd.to_datetime(df['TIME_BIN'], unit='s').dt.round('10min').dt.strftime('%H:%M:%S')
  print(df)
  
  OUTPUT:
           CITY  COUNT        DAY  TIME_BIN
  0     ATLANTA    476   Thursday  12:20:00
  1     PHOENIX     50   Saturday  15:40:00
  2       MIAMI    250     Friday  08:20:00
  3     CHICAGO    358     Monday  15:40:00
  4     PHOENIX    217   Thursday  22:10:00
  5       MIAMI     12   Thursday  21:40:00
  6      DENVER     22     Friday  10:30:00
  7     CHICAGO    645     Sunday  23:40:00
  8       MIAMI    188     Sunday  08:40:00
  </code></pre>
  
  <p>I want to make a separate data frame for each city and save it as a .csv. The code below works but how do I do it Pythonicly without having to explicitly state each city? Real data set has about 20 cities so I don't want to repaste this 20 times. I think the code below can be done in 1-2 lines using a for loop but I don't know what it would look like. Something like "for city in df['CITY']"</p>
  
  <pre><code>df_phoenix = df[df['CITY'] == "PHOENIX"]
  df_atlanta = df[df['CITY'] == "ATLANTA"]
  df_chicago = df[df['CITY'] == "CHICAGO"]
  df_phoenix.to_csv(os.getcwd() + "/data_phoenix.csv")
  df_atlanta.to_csv(os.getcwd() + "/data_atlanta.csv")
  df_chicago.to_csv(os.getcwd() + "/data_chicago.csv")
  </code></pre>