title: pandas - 'dataframe' object has no attribute 'str'
body: <p>I am trying to filter out the dataframe that contains a list of product. However,  I am getting the pandas - 'dataframe' object has no attribute 'str' error whenever I run the code.</p>
  
  <p>Here is the line of code:</p>
  
  <pre><code>include_clique = log_df.loc[log_df['Product'].str.contains("Product A")]
  </code></pre>
  
  <p>If anyone has any ideas of suggestions, please let me know. I've searched many times and I'm quite stuck. </p>
  
  <p>Product is an object datatype. </p>
  
  <p>EDIT:</p>
  
  <pre><code>import __future__
  import os
  import pandas as pd
  import numpy as np
  import tensorflow as tf
  import math
  
  
  
  data = pd.read_csv("FILE.csv", header = None)
  
  headerName=["DRID","Product","M24","M23","M22","M21","M20","M19","M18","M17","M16","M15","M14","M13","M12","M11","M10","M9","M8","M7","M6","M5","M4","M3","M2","M1"] 
  cliques = [(Confidential)] 
  data.columns=[headerName]
  
  
  log_df = data
  
  log_df = np.log(1+data[["M24","M23","M22","M21","M20","M19","M18","M17","M16","M15","M14","M13","M12","M11","M10","M9","M8","M7","M6","M5","M4","M3","M2","M1"]])
  copy = data[["DRID","Product"]].copy()
  log_df = copy.join(log_df)
  
  
  include_clique = log_df.loc[log_df['Product'].str.contains("Product A")]
  </code></pre>
  
  <p>Here is the head: </p>
  
  <pre><code>   ID    PRODUCT  M24      M23       M22     M21
  0  123421  A  0.000000  0.000000  1.098612  0.0   
  1  141840  A  0.693147  1.098612  0.000000  0.0   
  2  212006  A  0.693147  0.000000  0.000000  0.0   
  3  216097  A  1.098612  0.000000  0.000000  0.0   
  4  219517  A  1.098612  0.693147  1.098612  0.0
  </code></pre>
  
  <p>edit 2: here is print(data), A is the product. it looks like A is not under the category product when I print it out. </p>
  
  <pre><code>     DRID                         Product   M24   M23  M22  M21  M20  \
  0           52250  A                     0.0   0.0  2.0  0.0  0.0   
  1          141840  A                    1.0   2.0  0.0  0.0  0.0   
  2          212006  A                      1.0   0.0  0.0  0.0  0.0   
  3          216097  A                      2.0   0.0  0.0  0.0  0.0   
  </code></pre>