CODEWITHSUNDEEP
pythonpandas
MikeH
MikeH

Reputation: 47

Turning Panda Column into text file seperated by line break

I would like to create a txt file, where every line is a so called "ticker symbol" (=symbol for a stock). As a first step, I downloaded all the tickers I want via a wikipedia api:

import pandas as pd
import wikipedia as wp

html1 = wp.page("List of S&P 500 companies").html().encode("UTF-8")
df = pd.read_html(html1,header =0)[0]

df = df.drop(['SEC filings','CIK', 'Headquarters Location', 'Date first added', 'Founded'], axis = 1)
df.columns = df.columns.str.replace('Symbol', 'Ticker')

Secondly, I would like to create a txt file as mentionned above with all the ticker names of column "Ticker" from df. To do so, I probably have to do somithing similar to:

f = open("tickertest.txt","w+")
f.write("MMM\nABT\n...etc.")              
f.close()

Now my problem: Does anybody know how it is possible to bring my Ticker column from df into one big string where between every ticker there is a \n or every ticker is on a new line?

Upvotes: 1

Views: 1646

Answers (2)

tnknepp
tnknepp

Reputation: 6253

You can use to_csv for this. 

df.to_csv("test.txt", columns=["Ticker"], header=False, index=False)

This provides flexibility to include other columns, column names, and index values at some future point (should you need to do some sleuthing, or in case your boss asks for more information). You can even change the separator. This would be a simple modification (obvious changes, e.g.): 

df.to_csv("test.txt", columns=["Ticker", "Symbol",], header=True, index=True, sep="\t")

I think the benefit of this method over jfaccioni's answer is flexibility and ease of adapability. This also gets you away from explicitly opening a file. However, if you still want to explicitly open a file you should consider using "with", which will automatically close the buffer when you break out of the current indentation. e.g. 

with open("test.txt", "w") as fid:
    fid.write("MMM\nABT\n...etc.")

Upvotes: 2

jfaccioni
jfaccioni

Reputation: 7509

This should do the trick:

'\n'.join(df['Ticker'].astype(str).values)

Upvotes: 1

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