text: #!/usr/bin/env python3
  # -*- coding: utf-8 -*-
  """
  Created on Sun Oct 27 22:51:04 2019
  
  @author: robin
  """
  
  import pandas as pd
  import numpy as np
  import seaborn as sns
  import scipy
  import matplotlib.pyplot as plt
  from sklearn.metrics import mean_squared_error, r2_score
  from sklearn.preprocessing import MinMaxScaler
  from sklearn.preprocessing import LabelEncoder
  import plotly.graph_objects as go
  sns.set()
  file_name = input('file_name:')
  data = pd.read_csv('data/cleaned/' + file_name)
  data = data.drop('Unnamed: 0',axis = 1)
  data['Occur Time'] = data['Occur Time'].astype(str).str.rjust(4, fillchar="0")
  data = data[data['Occur Time'].astype(int) < 2359]  
  data = data[(data['Occur Time'].astype(int) % 100) < 59]
  encoder = LabelEncoder()
  data['Location'] = encoder.fit_transform(data['Location'].astype(str))
  data['Neighborhood'] = encoder.fit_transform(data['Neighborhood'].astype(str))
  data['UCR Literal'] = encoder.fit_transform(data['UCR Literal'].astype(str))
  data['NPU'] = encoder.fit_transform(data['NPU'].astype(str))
  data['Occur Date'] = pd.to_datetime(data['Occur Date'])
  data['IBR Code'] = encoder.fit_transform(data['IBR Code'].astype(str))
  dates = data['Occur Date'].apply(lambda x: x.strftime('%Y%m%d'))
  data['Occur Date'] = dates.astype(int).to_list()
  for i in range(data.shape[0]):
      try:    
          if data['zipcode'][i] == 'None':
                  data = data.drop(i, axis=0)
      except:
          pass
  index = list(data.columns)
  ax = sns.heatmap(data.astype(float).corr(), xticklabels= index, yticklabels=index)
  
  #plt.imshow(data.astype(float), cmap='hot', interpolation='nearest')
  #plt.show()
  