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I have the following DataFrame, where Track ID is the row index. How can I split the string in the stats column into 5 columns of numbers?
Track ID stats 14.0 (-0.00924175824176, 0.41, -0.742016492568, 0.0036830094242, 0.00251748449963) 28.0 (0.0411538461538, 0.318230769231, 0.758717081514, 0.00264000622468, 0.0106535783677) 42.0 (-0.0144351648352, 0.168438461538, -0.80870348637, 0.000816872566404, 0.00316572586742) 56.0 (0.0343461538462, 0.288730769231, 0.950844962874, 6.1608706775e-07, 0.00337262030771) 70.0 (0.00905164835165, 0.151030769231, 0.670257006716, 0.0121790506745, 0.00302182567957) 84.0 (-0.0047967032967, 0.171615384615, -0.552879463981, 0.0500316517755, 0.00217970256969) 
788
To split a column of tuples in a Python Pandas data frame, we can use the column's tolist method. We create the df data frame with the pd. DataFrame class and a dictionary. Then we create a new data frame from df by using df['b'].
split() Pandas provide a method to split string around a passed separator/delimiter. After that, the string can be stored as a list in a series or it can also be used to create multiple column data frames from a single separated string.
We can use str. split() to split one column to multiple columns by specifying expand=True option. We can use str. extract() to exract multiple columns using regex expression in which multiple capturing groups are defined.
To split cell into multiple rows in a Python Pandas dataframe, we can use the apply method. to call apply with a lambda function that calls str. split to split the x string value. And then we call explode to fill new rows with the split values.
And for the other case, assuming it are strings that look like tuples:
In [74]: df['stats'].str[1:-1].str.split(',', expand=True).astype(float) Out[74]: 0 1 2 3 4 0 -0.009242 0.410000 -0.742016 0.003683 0.002517 1 0.041154 0.318231 0.758717 0.002640 0.010654 2 -0.014435 0.168438 -0.808703 0.000817 0.003166 3 0.034346 0.288731 0.950845 0.000001 0.003373 4 0.009052 0.151031 0.670257 0.012179 0.003022 5 -0.004797 0.171615 -0.552879 0.050032 0.002180 (note: for older versions of pandas (< 0.16.1), you need to use return_type='frame' instead of the expand keyword)
By the way, if it are tuples and not strings, you can simply do the following:
pd.DataFrame(df['stats'].tolist(), index=df.index) If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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