Create two dataframes in pandas based on values in columns

Question

Given the following data

df = pd.DataFrame({"a": [1, 2, 3, 4, 5, 6, 7], "b": [4, 5, 9, 5, 6, 4, 0]})
df["split_by"] = df["b"].eq(9)

which looks as

a  b  split_by
0  1  4     False
1  2  5     False
2  3  9      True
3  4  5     False
4  5  6     False
5  6  4     False
6  7  0     False

I would like to create two dataframes as follows:

   a  b  split_by
0  1  4     False
1  2  5     False

and

   a  b  split_by
2  3  9      True
3  4  5     False
4  5  6     False
5  6  4     False
6  7  0     False

Clearly this is based on the value in column split_by, but I'm not sure how to subset using this.

My approach is:

split_1 = df.index < df[df["split_by"].eq(True)].index.to_list()[0]
split_2 = ~df.index.isin(split_1)

df1 = df[split_1]
df2 = df[split_2]

Space Impact · Accepted Answer · 2020-07-06 00:07:50Z

2

Use argmax as:

true_index = df['split_by'].argmax()
df1 = df.loc[:true_index-1, :]
df2 = df.loc[true_index:, :]

print(df1)
   a  b  split_by
0  1  4     False
1  2  5     False

print(df2)
   a  b  split_by
2  3  9      True
3  4  5     False
4  5  6     False
5  6  4     False
6  7  0     False

answered Jul 6, 2020 at 0:07

Space Impact

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Comments

Fab · Accepted Answer · 2020-07-06 00:21:58Z

1

Another approach:

i = df[df['split_by']==True].index.values[0]
df1 = df.iloc[:i]
df2 = df.iloc[i:]

This is assuming you have only one "True". If you have more than one "True", this code will split df into only two dataframes regardless, considering only the first "True".

answered Jul 6, 2020 at 0:21

Fab

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BENY · Accepted Answer · 2020-07-06 00:12:42Z

0

Use groupby with cumsum , notice if you have more then one True , this will split the dataframe to n+1 dfs (n True)

d={x : y for x , y in df.groupby(df.split_by.cumsum())}
d[0]
   a  b  split_by
0  1  4     False
1  2  5     False
d[1]
   a  b  split_by
2  3  9      True
3  4  5     False
4  5  6     False
5  6  4     False
6  7  0     False

answered Jul 6, 2020 at 0:12

BENY

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