CODEWITHSUNDEEP
pythonpandasdataframe
Pramesh
Pramesh

Reputation: 343

Pandas dataframe: Creating a new column based on data from other columns

I have a pandas dataframe, df:

   foo         bar
0  Supplies   Sample X
1  xyz        A   
2  xyz        B
3  Supplies   Sample Y
4  xyz        C
5  Supplies   Sample Z
6  xyz        D
7  xyz        E
8  xyz        F

I want to create a new df that looks something like this:

   bar
0  Sample X - A
1  Sample X - B
2  Sample Y - C
3  Sample Z - D
4  Sample Z - E
5  Sample Z - F

I am new to Pandas so I don't know how to achieve this. Could someone please help?

I tried DataFrame.iterrows but no luck.

Upvotes: 2

Views: 122

Answers (3)

PaulS
PaulS

Reputation: 25323

Another possible solution:

g = df.groupby(np.cumsum(df.bar.str.startswith('Sample')))
pd.DataFrame([x[1].bar.values[0] + ' - ' +
             y for x in g for y in x[1].bar.values[1:]], columns=['bar'])

Output:

            bar
0  Sample X - A
1  Sample X - B
2  Sample Y - C
3  Sample Z - D
4  Sample Z - E
5  Sample Z - F

Upvotes: 0

SomeDude
SomeDude

Reputation: 14228

You can do:

s = (df["bar"].mask(df.foo == "xyz").ffill() + "-" + df["bar"]).reindex(
    df.loc[df.foo == "xyz"].index
)

df = s.to_frame()

print(df):

           bar
1   Sample X-A
2   Sample X-B
4   Sample Y-C
6   Sample Z-D
7   Sample Z-E
8   Sample Z-F

Upvotes: 0

mozway
mozway

Reputation: 260335

You can use boolean indexing and ffill:

m = df['foo'].ne('Supplies')

out = (df['bar'].mask(m).ffill()[m]
       .add(' - '+df.loc[m, 'bar'])
       .to_frame().reset_index(drop=True)
       )

Output:

            bar
0  Sample X - A
1  Sample X - B
2  Sample Y - C
3  Sample Z - D
4  Sample Z - E
5  Sample Z - F

Upvotes: 4

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