CODEWITHSUNDEEP
pythonpandas
Sociopath
Sociopath

Reputation: 13401

Extracting values as a dictionary from dataframe based on list

I have a dataframe with unique value in each columns:

df1 = pd.DataFrame([["Phys","Shane","NY"],["Chem","Mark","LA"],
                    ["Maths","Jack","Mum"],["Bio","Sam","CT"]],
                    columns = ["cls1","cls2","cls3"])
print(df1)

    cls1    cls2    cls3
0   Phys    Shane   NY
1   Chem    Mark    LA
2   Maths   Jack    Mum
3   Bio     Sam     CT

And a list l1:

l1=["Maths","Bio","Shane","Mark"]
print(l1)

['Maths', 'Bio', 'Shane', 'Mark']

Now I want to retrieve a columns from dataframe that contains elements from list and list of elements.

Expected Output:

{'cls1' : ['Maths','Bio'], 'cls2': ['Shane','Mark']}

The code I have:

cls = []
for cols in df1.columns:
    mask = df1[cols].isin(l1)
    if mask.any():
        cls.append(cols)
print(cls)

The output of above code:

['cls1', 'cls2']

I'm struggling to get common elements from dataframe and list to convert it into dictionary.

Any suggestions are welcome.

Thanks.

Upvotes: 1

Views: 54

Answers (1)

jezrael
jezrael

Reputation: 862511

Use DataFrame.isin for mask, replace non match values by indexing and reshape with stack:

df = df1[df1.isin(l1)].stack()
print (df)
0  cls2    Shane
1  cls2     Mark
2  cls1    Maths
3  cls1      Bio
dtype: object

Last create list by dict comprehension:

d = {k:v.tolist() for k,v in df.groupby(level=1)}
print(d)
{'cls2': ['Shane', 'Mark'], 'cls1': ['Maths', 'Bio']}

Another solution:

d = {}
for cols in df1.columns:
    mask = df1[cols].isin(l1)
    if mask.any():
        d[cols] = df1.loc[mask, cols].tolist()

print(d)
{'cls2': ['Shane', 'Mark'], 'cls1': ['Maths', 'Bio']}

Upvotes: 2

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