create a new dataframe from selecting specific rows from existing dataframe python

Question

i have a table in my pandas dataframe. df

id count price
1    2     100
2    7      25
3    3     720
4    7     221
5    8     212
6    2     200

i want to create a new dataframe(df2) from this, selecting rows where count is 2 and price is 100,and count is 7 and price is 221

my output should be df2 =

id count price
1    2     100
4    7     221

i am trying using df[df['count'] == '2' & df['price'] == '100']

but getting error

TypeError: cannot compare a dtyped [object] array with a scalar of type [bool]

Asked By: Shubham R

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Answer 1

You nedd add () because & has higher precedence than ==:

df3 = df[(df['count'] == '2') & (df['price'] == '100')]
print (df3)
  id count price
0  1     2   100

If need check multiple values use isin:

df4 = df[(df['count'].isin(['2','7'])) & (df['price'].isin(['100', '221']))]
print (df4)
  id count price
0  1     2   100
3  4     7   221

But if check numeric, use:

df3 = df[(df['count'] == 2) & (df['price'] == 100)]
print (df3)

df4 = df[(df['count'].isin([2,7])) & (df['price'].isin([100, 221]))]
print (df4)

Answered By: jezrael

Answer 2

if you want to do by index id you could do:

new_df = (df.iloc[[1]]).append(df.iloc[[6]])

Answered By: chirob

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