title: Why is this task faster in Python than Julia?
body: <p>I ran the following code in RStudio:</p>
  <pre><code>exo &lt;- read.csv('exoplanets.csv',TRUE,&quot;,&quot;)
  df &lt;- data.frame(exo)
  
  ranks &lt;- 570
  files &lt;- 3198
  datas &lt;- vector()
  
  for ( w in 2:files ) {
      listas &lt;-vector()
      for ( i in 1:ranks) {
              name &lt;- as.character(df[i,w])
              listas &lt;- append (listas, name)
      }
      datas &lt;- append (datas, listas)
  }
  </code></pre>
  <p>It reads a huge NASA CSV file, converts it to a dataframe,
  converts each element to string, and adds them to a vector.</p>
  <p>RStudio took 4 min and 15 seconds.</p>
  <p>So I decided to implement the same code in Julia.
  I ran the following in VS Code:</p>
  <pre><code>using CSV, DataFrames
  
  df = CSV.read(&quot;exoplanets.csv&quot;, DataFrame)
  
  fil, col = 570, 3198
  arr = []
  
  for i in 2:fil
          for j in 1:col
              push!(arr, string(df[i, j]))
          end
  end
  </code></pre>
  <p>The result was good.
  The Julia code took only 1 minute and 25 seconds!</p>
  <p>Then for pure curiosity I implemented the same code
  this time in Python to compare.
  I ran the following in VS Code:</p>
  <pre><code>import numpy as np
  import pandas as pd
  
  exo = pd.read_csv(&quot;exoplanets.csv&quot;)
  arr = np.array(exo)
  
  fil, col = 570, 3198
  lis = []
  
  for i in range(1, fil):
          for j in range(col):
              lis.append(arr[i][j].astype('str'))
  </code></pre>
  <p>The result shocked me! Only 35 seconds!!!
  And in Spyder from Anaconda only 26 seconds!!!
  Almost 2 million floats!!!
  Is Julia slower than Python in data analysis?
  Can I improve the Julia code?</p>