Creating a big pandas Dataframe : <p>My code is retrieving historical data of 365 days back from today of 50 different stocks.</p>

<p>I want to store all those data in one dataframe to make it easier to analyse, here I want to filter all those data, date wise and calculate number of advancing/declining stocks at a given date.</p>

<p>My code:</p>

<pre><code>import datetime
from datetime import date, timedelta
import pandas as pd
import nsepy as ns

#setting default dates
end_date = date.today()
start_date = end_date - timedelta(365)

#Deriving the names of 50 stocks in Nifty 50 Index
nifty_50 = pd.read_html('https://en.wikipedia.org/wiki/NIFTY_50')

nifty50_symbols = nifty_50[1][1]




for x in nifty50_symbols:
    data = ns.get_history(symbol = x, start=start_date, end=end_date)
    big_df = pd.concat(data)
</code></pre>

<p>Output:</p>

<pre><code>Traceback (most recent call last):
  File "F:\My\Getting data from NSE\advances.py", line 27, in &lt;module&gt;
    big_df = pd.concat(data)
  File "C:\Users\Abinash\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\reshape\concat.py", line 212, in concat
    copy=copy)
  File "C:\Users\Abinash\AppData\Local\Programs\Python\Python36\lib\site-packages\pandas\core\reshape\concat.py", line 227, in __init__
    '"{name}"'.format(name=type(objs).__name__))
TypeError: first argument must be an iterable of pandas objects, you passed an object of type "DataFrame"
</code></pre>

<p>I am very new to python, I went through the tutorial of pandas and saw that pandas.concat was used to merge multiple dataframes into one. I might have understood that wrong.</p>
