title: Read multiple file csv in pandas from a directory and store them in a array of list, each file as one observation
body: <p>I have a folder, i have 30 CSV files in there , all different name.</p>
  
  <p>I want to loop through all the files and read them using pandas separately, and store them in a list of lists. While reading them separately, I want also to remove some variables from them at the same like remove columns which are correlated.</p>
  
  <p>Currently, I am trying to do this.</p>
  
  <pre><code>import glob
  import pandas as pd
  path = os.getcwd()
  
  
  # Get folder path containing text files
  file_list = glob.glob(path + '/*.csv')
  data = []
  for file_path in file_list:
      data.append(
          pd.read_csv(file_path).drop(['column1', 'column2'], axis =1))
  # now you can access it outside the "for loop..."
  for d in data:
      print(d) 
  </code></pre>
  
  <p>So, I want to store each data frame as 2 D lists in a list and train my model because each[ [CSV][2] ][3]file is an observation. My CSV file is having(5000,12) observations. have label for each CSV or instance, which is the filename.</p>
  
  <p>Don't know If I am moving in right direction.</p>
  
  <pre><code>len(data) 
  # 30
  
  label = [1,1,1,1,1,1,1,1,1,1,,2,2,2,2,2,2,2,2,2,2,3,3,3,3,3,3,3,3,3,3]
  max_length = 25 # shape of data frame after removing two variables
  # define the model
  
  model = Sequential()
  model.add(Dense(24, input_dim=25, activation='relu'))
  model.add(Dense(1, activation='sigmoid'))
  # compile the model
  model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['acc'])
  # summarize the model
  print(model.summary())
  </code></pre>