CODEWITHSUNDEEP
pythonpandasdictionarydataframeindexing
Billy
Billy

Reputation: 33

Filter dataframe based on difference been two series, one mapped via dictionary

I have my dictionary

d = {'A':1, 'B':2, 'C':3}

and my dataframe

df =pd.DataFrame({
"col1": ["A", "B", "C"],
"col2": [1, 2, 3],
"col3": [2, 1, 4] })

I search to compare each value in df with the correspondant value in the dictionary. If it matches the value is kept, otherwise the value is drop.

I try 

m = df['col2'] >= d[df['col1']]
df.where(m, df, other = "")

But it get this error code for m: TypeError: 'Series' objects are mutable, thus they cannot be hashed...

Thank you for your help.

Upvotes: 3

Views: 166

Answers (3)

John R
John R

Reputation: 1508

Create a new column for comparison using apply

df[‘dict_col’] = df[‘col1’].apply(lambda k: d[k])

m = df[‘dict_col’] >= df[‘col2’]

df[‘col2’] = df[‘col2’].where(m, df, other = "")

Upvotes: 1

narendra-choudhary
narendra-choudhary

Reputation: 4826

Hint is there in error message itself.

TypeError: 'Series' objects are mutable, thus they cannot be hashed.

df['col1'] is a Series object, which is a mutable object.

Mutable objects cannot be hashed and hence cannot be used as a dictionary key. From docs:

... dictionaries are indexed by keys, which can be any immutable type; strings and numbers can always be keys...

You are using Series object as dictionary key. One way to rewrite d[df['col1']] is:

[d[x] for x in df['col1']]

Upvotes: 1

jpp
jpp

Reputation: 164823

You can use pd.Series.map with loc and Boolean indexing:

df = df.loc[df['col2'] >= df['col1'].map(d)]

Upvotes: 1

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