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I'm a beginner in both Python and Pandas module. I'm working on a statistics problem in which I want to merge two dataframes with specific styles.

Here is my 1st dataframe for the mean values:

- 5.006 3.418 1.464 0.244 - 5.936 2.770 4.260 1.326 - 6.588 2.974 5.552 2.026

And then is the 2nd dataframe for the std values:

 - 0.352490 0.381024 0.173511 0.107210 - 0.516171 0.313798 0.469911 0.197753 - 0.635880 0.322497 0.551895 0.274650

So are there any ways to merge the two dataframes so the final output would look like "mean"±"std"? such as "5.006 ± 0.352490"?

Thank you!

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2 Answers 2

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Convert to string using .astype and then a simple concatenation should suffice.

out = df.astype(str) + ' ± ' + df2.astype(str) print(out) 0 1 2 3 0 5.006 ± 0.35249 3.418 ± 0.381024 1.464 ± 0.173511 0.244 ± 0.10721 1 5.936 ± 0.516171 2.77 ± 0.313798 4.26 ± 0.469911 1.326 ± 0.197753 2 6.588 ± 0.63588 2.974 ± 0.322497 5.552 ± 0.551895 2.026 ± 0.27465

Works nicely provided you have the same index and columns across both dataframes. If not, you can do set one to the other:

df2.index = df.index df2.columns = df.columns out = df.astype(str) + ' ± ' + df2.astype(str)

Details:

df 0 1 2 3 0 5.006 3.418 1.464 0.244 1 5.936 2.770 4.260 1.326 2 6.588 2.974 5.552 2.026 df2 0 1 2 3 0 0.352490 0.381024 0.173511 0.107210 1 0.516171 0.313798 0.469911 0.197753 2 0.635880 0.322497 0.551895 0.274650
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2 Comments

Thank you Coldspeed. Do you know how to transform the outputed table to a figure, like png or jpeg?
@Yujian I don't, but there's a useful link here: stackoverflow.com/questions/35634238/… AND stackoverflow.com/questions/26678467/…
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You need concatenate both df, need same index and columns names:

df1.astype(str) + ' ± ' + df2.astype(str)

Another solution:

df1.astype(str).add(' ± ').add(df2.astype(str)) 
df = df1.astype(str) + ' ± ' + df2.astype(str) print (df) 0 1 2 3 0 -5.006 ± -0.35249 3.418 ± 0.381024 1.464 ± 0.173511 0.244 ± 0.10721 1 -5.936 ± -0.516171 2.77 ± 0.313798 4.26 ± 0.469911 1.326 ± 0.197753 2 -6.588 ± -0.63588 2.974 ± 0.322497 5.552 ± 0.551895 2.026 ± 0.27465
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