CODEWITHSUNDEEP
pythonpython-3.xpandasdataframe
gggert
gggert

Reputation: 176

how to return one column based on a condition depending on a different column

I would like to add new column to a DataFrame based on a condition.

df = pd.DataFrame({'category' : ['A', 'A', 'B', 'C'],
               'value': [10,10,20,20]
                   })

On DataFrame above, I would like to add separated columns ['Value A'], ['Value B'], ['Value C'] to make calculations easier. Desired output would be like

df = pd.DataFrame({'category' : ['A', 'A', 'B', 'C'],
               'value': [10,10,20,20]
               'Value A' : [10, 10, 0, 0]
               'Value B' : [0, 0, 20, 0]
               'Value C' : [0, 0, 0, 20]
                   })

I thought I could just do it by

df['Value A'] = df['value'](add some condition here).fillna(0)

But seems like it can be done this simple. Is there any way to do that?

Upvotes: 2

Views: 227

Answers (2)

Quang Hoang
Quang Hoang

Reputation: 150805

Let's try:

df.join(df.reset_index()
   .pivot_table(index='index', columns='category', 
                values='value', fill_value=0)
   .reindex(['A','B','C'], axis=1, fill_value=0)         # from comment
   .add_prefix('value')
)

Output:

  category  value  valueA  valueB  valueC
0        A     10      10       0       0
1        A     10      10       0       0
2        B     20       0      20       0
3        C     20       0       0      20

Upvotes: 1

sammywemmy
sammywemmy

Reputation: 28729

You can create a dictionary pairing the new column names with the conditions:

condition = {f"Value {letter}": df.loc[df.category.eq(letter), "value"]
             for letter in df.category.unique()}

condition

{'Value A': 0    10
 1    10
 Name: value, dtype: int64,
 'Value B': 2    20
 Name: value, dtype: int64,
 'Value C': 3    20
 Name: value, dtype: int64}

Update: Lovely suggestion from @QuangHoang - you can build the condition dictionary with a groupby :

 condition = {f"Value {k}": d for k, d in df.groupby("category")["value"]}

Now unpack the condition via assign:

df.assign(**condition).fillna(0)

    category    value   Value A Value B Value C
0      A          10    10.0     0.0    0.0
1      A          10    10.0     0.0    0.0
2      B          20    0.0     20.0    0.0
3      C          20    0.0      0.0    20.0

Upvotes: 2

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