title: How to correctly append rows from a df to another based on timestamps on Python? Pandas related
body: <p>I currently have 253 csv files with the following filenames:</p>
  <pre><code>1. ALICEUSDT-1h-2021-06-01.csv
  2. ALICEUSDT-1h-2021-06-02.csv
  3. ALICEUSDT-1h-2021-06-03.csv
  4. ALICEUSDT-1h-2021-06-06.csv
  5. ALICEUSDT-1h-2021-06-09.csv
  6. ALICEUSDT-1h-2021-06-11.csv
  7. ALICEUSDT-1h-2021-06-12.csv
  .
  .
  .
  253. ALICEUSDT-1h-2022-02-13.csv
  </code></pre>
  <p>located at this path: <code>C:\Users\StoreX\AliceUSDT-Mark_Prices_Klines_1h_Timeframe</code></p>
  <p>Each of those files contains the hourly price action data for a particular asset, having in total 24 rows (<strong>without column names nor indexes</strong>)</p>
  <p>Any of those dataframes look like this:</p>
  <pre><code>    1623369600000   5.53548967  5.8         5.4473946   5.4719309   0   1623373199999   0   3600    0   0   0
      1623373200000   5.47278238  5.53195431  5.37178569  5.41425374  0   1623376799999   0   3600    0   0   0
      1623376800000   5.41425372  5.439       5.30612058  5.43896918  0   1623380399999   0   3597    0   0   0
      1623380400000   5.439       5.573       5.439       5.51534579  0   1623383999999   0   3600    0   0   0
      1623384000000   5.51534611  5.75008683  5.46536508  5.699       0   1623387599999   0   3600    0   0   0
      1623387600000   5.699       5.7319193   5.59313511  5.63882188  0   1623391199999   0   3582    0   0   0
      1623391200000   5.63892904  5.65847764  5.50099143  5.52530541  0   1623394799999   0   3600    0   0   0
      1623394800000   5.52201075  5.69104913  5.49124857  5.658       0   1623398399999   0   3600    0   0   0
      1623398400000   5.6554082   6.336       5.56700781  6.2605508   0   1623401999999   0   3600    0   0   0
      1623402000000   6.26056029  6.57769402  6.26056029  6.392       0   1623405599999   0   3600    0   0   0
      1623405600000   6.3935684   6.68259312  6.30951189  6.582       0   1623409199999   0   3600    0   0   0
      1623409200000   6.58        6.633       6.41970848  6.55150417  0   1623412799999   0   3600    0   0   0
      1623412800000   6.55051699  6.56212633  6.34272711  6.44329682  0   1623416399999   0   3600    0   0   0
      1623416400000   6.43806578  6.63709121  6.40735932  6.49129756  0   1623419999999   0   3600    0   0   0
      1623420000000   6.49097939  6.498       6.19692729  6.25924944  0   1623423599999   0   3600    0   0   0
      1623423600000   6.25924944  6.2822904   6.05089683  6.13788357  0   1623427199999   0   3600    0   0   0
      1623427200000   6.13764793  6.228       6.005       6.015432    0   1623430799999   0   3600    0   0   0
      1623430800000   6.015       6.198       5.97772068  6.19544081  0   1623434399999   0   3600    0   0   0
      1623434400000   6.198       6.349       6.13209062  6.2308081   0   1623437999999   0   3600    0   0   0
      1623438000000   6.23083904  6.42623752  6.201       6.27776139  0   1623441599999   0   3600    0   0   0
      1623441600000   6.27776137  6.31281154  6.02227423  6.036       0   1623445199999   0   3600    0   0   0
      1623445200000   6.033       6.185       5.90345962  6.119       0   1623448799999   0   3600    0   0   0
      1623448800000   6.119       6.298       6.047       6.20634564  0   1623452399999   0   3600    0   0   0
      1623452400000   6.20606916  6.20606916  5.9779765   6.05824449  0   1623455999999   0   3600    0   0   0
  </code></pre>
  <blockquote>
  <p>The <a href="https://www.unixtimestamp.com/" rel="nofollow noreferrer">Unix Timestamp</a> is used to set the exact date at which the
  price was opened (2nd column), and also to set the exact date at which
  it was closed (5th column), the columns with 0 and ≈3600 values do not
  matter.</p>
  <p>The 2nd column corresponds to the Open price</p>
  <p>The 3rd column corresponds to the High price</p>
  <p>The 4th column corresponds to the Low price</p>
  <p>The 5th column corresponds to the Close price</p>
  </blockquote>
  <p>I wanted to export a big df with all of the rows from these files, and to do so, I wrote the following code:</p>
  <pre><code>import pandas as pd
  import os
  
  def check_path(infile):
      return os.path.exists(infile)   
  
  first_entry = input('Tell me the path where your csv files are located at: ')
  
  while True:
      
      if check_path(first_entry) == False:
          print('\n')
          print('This PATH is invalid!')
          first_entry = input('Tell me the RIGHT PATH in which your csv files are located: ')
          
      elif check_path(first_entry) == True:
          print('\n')
          final_output = first_entry
          break
  
  big_df = pd.DataFrame() 
  for name in os.listdir(first_entry):
      if name.endswith(&quot;.csv&quot;):
          print(name)
          current_df_path = first_entry+&quot;\\&quot;+name
          df = pd.read_csv(current_df_path, header=None)
          big_df.append(df)
          print(big_df)
  print(&quot;&quot;)        
  print(&quot;the final df is the following&quot;)
  print(&quot;&quot;)
  print(big_df)
  </code></pre>
  <p>However, it didn't work at all after run, the output was the following:</p>
  <blockquote>
  <p>.</p>
  <p>.</p>
  <p>.</p>
  <p>ALICEUSDT-1h-2022-02-12.csv Empty DataFrame Columns: [] Index: []
  ALICEUSDT-1h-2022-02-13.csv Empty DataFrame Columns: [] Index: []</p>
  <p>the final df is the following</p>
  <p>Empty DataFrame Columns: [] Index: []</p>
  </blockquote>
  <p>So, I would like to know what did I do wrong? Why isn't the <code>big_df</code> actually full of information?</p>
  <p>And finally, how could the code above improved to do what's demanded?</p>