CODEWITHSUNDEEP
pandas
martinbshp
martinbshp

Reputation: 1163

Add a series to existing DataFrame

I created the following DataFrame:

purchase_1 = pd.Series({'Name': 'Chris',
                        'Item Purchased': 'Dog Food',
                        'Cost': 22.50})
purchase_2 = pd.Series({'Name': 'Kevyn',
                        'Item Purchased': 'Kitty Litter',
                        'Cost': 2.50})
purchase_3 = pd.Series({'Name': 'Vinod',
                        'Item Purchased': 'Bird Seed',
                        'Cost': 5.00})

df = pd.DataFrame([purchase_1, purchase_2, purchase_3], index=['Store 1', 'Store 1', 'Store 2'])

I then added the following column:

df['Location'] = df.index
df

How do I then add the following series to the my DataFrame? Thank you.

s = pd.Series({'Name':'Kevyn', 'Item Purchased': 'Kitty Food', 'Cost': 3.00, 'Location': 'Store 2'})

Upvotes: 17

Views: 78766

Answers (3)

sargupta
sargupta

Reputation: 1033

I hope it will be helpful and give you the accurate result, 

purchase_4 = pd.Series({'Name': 'Kevyn', 
                        'Item Purchased': 'Kitty Food', 
                        'Cost': 3.00,
                       'Location': 'Store 2'})
df2 = df.append(purchase_4, ignore_index=True)
df2.set_index(['Location', 'Name'])

Upvotes: 2

Jaroslav Borovský
Jaroslav Borovský

Reputation: 1

Solution directly from the source of your question. 

df = df.set_index([df.index, 'Name'])
df.index.names = ['Location', 'Name']
df = df.append(pd.Series(data={'Cost': 3.00, 'Item Purchased': 'Kitty Food'}, name=('Store 2', 'Kevyn')))
df

Upvotes: 0

jezrael
jezrael

Reputation: 862591

Use concat + to_frame + T:

df = pd.concat([df, s.to_frame().T])
print (df)
         Cost Item Purchased Location   Name
Store 1  22.5       Dog Food  Store 1  Chris
Store 1   2.5   Kitty Litter  Store 1  Kevyn
Store 2     5      Bird Seed  Store 2  Vinod
0           3     Kitty Food  Store 2  Kevyn

Also for default index is possible add parameter ignore_index=True:

df = pd.concat([df, s.to_frame().T], ignore_index=True)
print (df)
   Cost Item Purchased Location   Name
0  22.5       Dog Food  Store 1  Chris
1   2.5   Kitty Litter  Store 1  Kevyn
2     5      Bird Seed  Store 2  Vinod
3     3     Kitty Food  Store 2  Kevyn

Or add some new index value which is not in original df with loc:

df.loc[0] = s
print (df)
         Cost Item Purchased   Name Location
Store 1  22.5       Dog Food  Chris  Store 1
Store 1   2.5   Kitty Litter  Kevyn  Store 1
Store 2   5.0      Bird Seed  Vinod  Store 2
0         3.0     Kitty Food  Kevyn  Store 2

because else values are overwritten by Series:

df.loc['Store 2'] = s
print (df)
         Cost Item Purchased   Name Location
Store 1  22.5       Dog Food  Chris  Store 1
Store 1   2.5   Kitty Litter  Kevyn  Store 1
Store 2   3.0     Kitty Food  Kevyn  Store 2 <- overwritten row

Upvotes: 32

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