CODEWITHSUNDEEP
pythonpandasdataframe
jeangelj
jeangelj

Reputation: 4498

new dataframe based on column from existing python pandas

I am trying to create a new dataframes df_A, df_B and df_C from an existing dataframe df based on categorical values in the column category (A,B and C).

This doesn't work

df_A = {n: df.ix[rows] 
 for n, rows in enumerate(df.groupby('Category').groups)}

Here I get the error "Key Error: A"

(Note: A is one of the categories)

This doesn't work either

df_A = np.where(df['Category']=='A')).copy()

Here I get the error: "syntax error"

Finally, this doesn't work

df_A = np.where(raw[raw['Category']=='A']).copy()

"AttributeError: 'tuple' object has no attribute 'copy'"

Thank You

Upvotes: 2

Views: 1474

Answers (1)

jezrael
jezrael

Reputation: 863631

It seems you need first boolean indexing because Category is column, not index if need dictionary :

df2 = {n: data[ data['Category'] == rows] 
           for n, rows in enumerate(data.groupby('Category').groups)}

Or try remove groups:

df2 = {n: rows[1] for n, rows in enumerate(data.groupby('Category'))}

Sample:

data = pd.DataFrame({'Category':['A','A','D'],
                   'B':[4,5,6],
                   'C':[7,8,9]})

print (data)
   B  C Category
0  4  7        A
1  5  8        A
2  6  9        D

df2 = {n: rows[1] for n, rows in enumerate(data.groupby('Category'))}
print (df2)
{0:    B  C Category
0  4  7        A
1  5  8        A, 1:    B  C Category
2  6  9        D}

df2 = {n: data[ data['Category'] == rows] 
           for n, rows in enumerate(data.groupby('Category').groups)}
print (df2)
{0:    B  C Category
0  4  7        A
1  5  8        A, 1:    B  C Category
2  6  9        D}

Solution without groupby

df2 = {n: data[data['Category'] == rows] for n, rows in enumerate(data['Category'].unique())}
print (df2)
{0:    B  C Category
0  4  7        A
1  5  8        A, 1:    B  C Category
2  6  9        D}

print (df2[0])
   B  C Category
0  4  7        A
1  5  8        A

But if need select dict of DataFrame by Category value:

dfs = {n: rows for n, rows in data.groupby('Category')}
print (dfs)
{'A':    B  C Category
0  4  7        A
1  5  8        A, 'D':    B  C Category
2  6  9        D}

print (dfs['A'])
   B  C Category
0  4  7        A
1  5  8        A

Upvotes: 1

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