CODEWITHSUNDEEP
pythonpandas
SBad
SBad

Reputation: 1345

Update Pandas series based on condition in a separate series

All,

I would like to update an existing column in pandas. Here is an example:

df = pd.DataFrame({'a': ['A', 'B', 'C', 'A'], 'b': ['C', 'A', 'B', 'C'], 'c': np.random.randn(4), 'd': np.random.randn(4) })

df['NewColumn1'] = np.where( df['a'].str.contains('A') & df['b'].str.contains('C') , df['c'], 1)

df
   a  b         c         d  NewColumn1
0  A  C -0.668001 -0.434521   -0.668001
1  B  A  1.893248  1.927666    1.000000
2  C  B -2.145310  0.602808    1.000000
3  A  C  0.323770  0.966303    0.323770

I have create a new column called 'NewColumn1'.

Now I would like to update NewColumn1 based on new condition (my aim here is to update NewColumn1 not overwrite its previous values).

I would like to update 'NewColumn1' and set its values to 100 when column 'd' is bigger than 1. How can I do that?

Many Thanks

Upvotes: 1

Views: 1809

Answers (2)

jezrael
jezrael

Reputation: 863801

You need:

df['NewColumn1'] = np.where( df['d'] > 1 , 100, df['NewColumn1'])

Upvotes: 1

jpp
jpp

Reputation: 164853

You can use .loc accessor to update an existing series:

df.loc[df['d'] > 1, 'NewColumn1'] = 100

Your question is still unclear: "Update but not overwrite previous values" does not make sense.

In future, provide your desired output. This is much more valuable than a lengthy description.

Upvotes: 2

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