CODEWITHSUNDEEP
pythonpandassplit
t_n
t_n

Reputation: 343

Split strings in tuples into columns, in Pandas

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)

Upvotes: 30

Views: 42009

Answers (4)

G-Higgins
G-Higgins

Reputation: 201

Since your stats column contains tuples of length 5, this does the job fairly succinctly

pd.concat([df['Track ID'], pd.DataFrame(df['stats'].tolist(), index = 
df.index, columns = [f'stats_{i}' for i in range(1, 6)])], axis = 1)

Upvotes: 0

joris
joris

Reputation: 139162

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)

Upvotes: 59

TheBlackCat
TheBlackCat

Reputation: 10298

If you have a sequence of tuples and not strings, and you want them as DataFrame columns, this is the simplest approach:

df = pd.concat([df['Track ID'],pd.DataFrame(df['stats'].values.tolist())], axis=1)

If it is actually strings, you can first convert it to lists like so, then apply the above operation:

dfpart = pd.DataFrame(df['stats'].apply(lambda x: x.strip('()').split(', ')).values.tolist()).astype(float)
df = pd.concat([df['Track ID'], dfpart], axis=1)

Upvotes: 2

chthonicdaemon
chthonicdaemon

Reputation: 19760

Assuming you have a column which contains tuples (as it appears in your example) rather than strings, this will work:

df = pandas.DataFrame({'Track ID': [14, 28, 42], 'stats': [(1, 2, 3, 4, 5), (1, 2, 3, 4, 5), (1, 2, 3, 4, 5)]}).set_index("Track ID")

from operator import itemgetter
for i in range(5):
    df["Col {}".format(i)] = df.stats.apply(itemgetter(i))

If you actually have strings that look like tuples, you can parse them first and then apply the same pattern as above:

df = df2 = pandas.DataFrame({'Track ID': [14, 28, 42], 'stats': ["(1, 2, 3, 4, 5)", "(1, 2, 3, 4, 5)", "(1, 2, 3, 4, 5)"]}).set_index("Track ID")
df.stats = df2.stats.str.strip("()").str.split(", ")

Upvotes: 0

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