title: pandas column value and variable values string concat
body: <p>I have following code</p>
  
  <pre><code>import requests
  import json
  import pandas as pd
  from datetime import datetime
  from datetime import timedelta
  pd.options.display.float_format = '{:,.2f}'.format
  
  url="https://nseindia.com/api/equity-stockIndices?index=SECURITIES%20IN%20F%26O"
  
  headers = { "Accept-Encoding":"gzip, deflate","Accept-Language":"en-US,en;q=0.9",
              "User-Agent":"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36 OPR/65.0.3467.78"}
  
  r=requests.get(url, headers=headers).json()
  df1=pd.DataFrame().from_records(r['data'])
  df2=df1[['symbol','dayHigh','dayLow','lastPrice','yearHigh','yearLow','previousClose','change','pChange']]
  df2=df2.sort_values(['symbol'], ascending=True).reset_index(drop=True)
  nifty1=df2.round(2)
  nifty1
  dt1 = '01-01-2020'
  dt2 = '11-01-2020'
  </code></pre>
  
  <p>Generating following output</p>
  
  <pre><code>    symbol  dayHigh dayLow  lastPrice   yearHigh    yearLow previousClose   change  pChange
  0   ACC 1525    1487.25 1510.8  1769.05 1326    1486.15 24.65   1.66
  1   ADANIENT    213 205.9   210.5   221.5   113 208 2.50    1.20
  2   ADANIPORTS  394.3   390 391.3   430.6   292.1   391.8   -0.50   -0.13
  ...
  </code></pre>
  
  <p>I have following string for</p>
  
  <pre><code>https://nseindia.com/api/historical/cm/equity?symbol=XXXX&amp;series=["EQ"]&amp;from=fromdt&amp;to=todt
  </code></pre>
  
  <p>I want to replace XXXX by nifty1['symbol'] column, preferably without space and fromdt and todt with dt1 and dt2</p>
  
  <p>eg.</p>
  
  <pre><code>https://nseindia.com/api/historical/cm/equity?symbol=ACC&amp;series=["EQ"]&amp;from=01-01-2020&amp;to=11-01-2020
  https://nseindia.com/api/historical/cm/equity?symbol=ADANIENT&amp;series=["EQ"]&amp;from=01-01-2020&amp;to=11-01-2020
  https://nseindia.com/api/historical/cm/equity?symbol=ADANIPORTS&amp;series=["EQ"]&amp;from=01-01-2020&amp;to=11-01-2020
  </code></pre>
  
  <p>and store into new dataframe</p>