Drop rows on multiple conditions in pandas dataframe
My df has 3 columns
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
I want to drop rows where df.col_1 is 1.0 and df.col_2 is 0.0. So, I would get:
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 0.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.22, 3.11),
"col_3": ("Mon", "Tue", "Thu", "Mon", "Tue")})
I tried:
df_new = df.drop[df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index]
It gives me the error:
'method' object is not subscriptable
Any idea how to solve the above problem?
python pandas
edited Nov 29 at 6:08
Saurabh
2,08321726
asked Sep 22 at 12:57
Dsh M
297
add a comment |
My df has 3 columns
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
I want to drop rows where df.col_1 is 1.0 and df.col_2 is 0.0. So, I would get:
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 0.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.22, 3.11),
"col_3": ("Mon", "Tue", "Thu", "Mon", "Tue")})
I tried:
df_new = df.drop[df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index]
It gives me the error:
'method' object is not subscriptable
Any idea how to solve the above problem?
python pandas
edited Nov 29 at 6:08
Saurabh
2,08321726
asked Sep 22 at 12:57
Dsh M
297
add a comment |
My df has 3 columns
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
I want to drop rows where df.col_1 is 1.0 and df.col_2 is 0.0. So, I would get:
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 0.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.22, 3.11),
"col_3": ("Mon", "Tue", "Thu", "Mon", "Tue")})
I tried:
df_new = df.drop[df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index]
It gives me the error:
'method' object is not subscriptable
Any idea how to solve the above problem?
python pandas
edited Nov 29 at 6:08
Saurabh
2,08321726
asked Sep 22 at 12:57
Dsh M
297
My df has 3 columns
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
I want to drop rows where df.col_1 is 1.0 and df.col_2 is 0.0. So, I would get:
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 0.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.22, 3.11),
"col_3": ("Mon", "Tue", "Thu", "Mon", "Tue")})
I tried:
df_new = df.drop[df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index]
It gives me the error:
'method' object is not subscriptable
Any idea how to solve the above problem?
python pandas
python pandas
edited Nov 29 at 6:08
Saurabh
2,08321726
asked Sep 22 at 12:57
Dsh M
297
edited Nov 29 at 6:08
Saurabh
2,08321726
asked Sep 22 at 12:57
Dsh M
297
edited Nov 29 at 6:08
Saurabh
2,08321726
edited Nov 29 at 6:08
Saurabh
2,08321726
edited Nov 29 at 6:08
Saurabh
2,08321726
2,08321726
asked Sep 22 at 12:57
Dsh M
297
asked Sep 22 at 12:57
Dsh M
297
asked Sep 22 at 12:57
Dsh M
297
297
add a comment |
add a comment |
3 Answers
3
active
oldest
votes
drop is a method, you are calling it using , that is why it gives you:
'method' object is not subscriptable
change to () (a normal method call) an it should work:
import pandas as pd
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
df_new = df.drop(df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index)
print(df_new)
Output
col_1 col_2 col_3
0 0.0 0.00 Mon
1 0.0 0.24 Tue
2 1.0 1.00 Thu
4 0.0 0.22 Mon
5 1.0 3.11 Tue
answered Sep 22 at 13:03
Daniel Mesejo
12.4k1924
Awesome! Thanks a lot.
– Dsh M
Sep 22 at 16:51
add a comment |
Try to filter your df with loc. It's so powerfull.
The "~" means you want the opposit of your condition.
The ":" means you want to keep all the columns
df = df.loc[~((df['col_1'] == 1.0) & (df['col_2'] == 0.0)),:]
answered Sep 22 at 13:00
Charles R
825213
add a comment |
You can use or (|) operator for this ,
Refer this link for it pandas: multiple conditions while indexing data frame - unexpected behavior
i.e dropping rows where both conditions are met
df = df.loc[~((df['col_1']==1) | (df['col_2']==0))]
answered Nov 22 at 18:03
Saurabh
2,08321726
add a comment |
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3 Answers
3
active
oldest
votes
3 Answers
3
active
oldest
votes
active
oldest
votes
active
oldest
votes
drop is a method, you are calling it using , that is why it gives you:
'method' object is not subscriptable
change to () (a normal method call) an it should work:
import pandas as pd
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
df_new = df.drop(df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index)
print(df_new)
Output
col_1 col_2 col_3
0 0.0 0.00 Mon
1 0.0 0.24 Tue
2 1.0 1.00 Thu
4 0.0 0.22 Mon
5 1.0 3.11 Tue
answered Sep 22 at 13:03
Daniel Mesejo
12.4k1924
Awesome! Thanks a lot.
– Dsh M
Sep 22 at 16:51
add a comment |
drop is a method, you are calling it using , that is why it gives you:
'method' object is not subscriptable
change to () (a normal method call) an it should work:
import pandas as pd
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
df_new = df.drop(df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index)
print(df_new)
Output
col_1 col_2 col_3
0 0.0 0.00 Mon
1 0.0 0.24 Tue
2 1.0 1.00 Thu
4 0.0 0.22 Mon
5 1.0 3.11 Tue
answered Sep 22 at 13:03
Daniel Mesejo
12.4k1924
Awesome! Thanks a lot.
– Dsh M
Sep 22 at 16:51
add a comment |
drop is a method, you are calling it using , that is why it gives you:
'method' object is not subscriptable
change to () (a normal method call) an it should work:
import pandas as pd
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
df_new = df.drop(df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index)
print(df_new)
Output
col_1 col_2 col_3
0 0.0 0.00 Mon
1 0.0 0.24 Tue
2 1.0 1.00 Thu
4 0.0 0.22 Mon
5 1.0 3.11 Tue
answered Sep 22 at 13:03
Daniel Mesejo
12.4k1924
drop is a method, you are calling it using , that is why it gives you:
'method' object is not subscriptable
change to () (a normal method call) an it should work:
import pandas as pd
df = pd.DataFrame({"col_1": (0.0, 0.0, 1.0, 1.0, 0.0, 1.0, 1.0),
"col_2": (0.0, 0.24, 1.0, 0.0, 0.22, 3.11, 0.0),
"col_3": ("Mon", "Tue", "Thu", "Fri", "Mon", "Tue", "Thu")})
df_new = df.drop(df[(df['col_1'] == 1.0) & (df['col_2'] == 0.0)].index)
print(df_new)
Output
col_1 col_2 col_3
0 0.0 0.00 Mon
1 0.0 0.24 Tue
2 1.0 1.00 Thu
4 0.0 0.22 Mon
5 1.0 3.11 Tue
answered Sep 22 at 13:03
Daniel Mesejo
12.4k1924
answered Sep 22 at 13:03
Daniel Mesejo
12.4k1924
answered Sep 22 at 13:03
Daniel Mesejo
12.4k1924
answered Sep 22 at 13:03
Daniel Mesejo
12.4k1924
12.4k1924
Awesome! Thanks a lot.
– Dsh M
Sep 22 at 16:51
add a comment |
Awesome! Thanks a lot.
– Dsh M
Sep 22 at 16:51
Awesome! Thanks a lot.
– Dsh M
Sep 22 at 16:51
Awesome! Thanks a lot.
– Dsh M
Sep 22 at 16:51
add a comment |
Try to filter your df with loc. It's so powerfull.
The "~" means you want the opposit of your condition.
The ":" means you want to keep all the columns
df = df.loc[~((df['col_1'] == 1.0) & (df['col_2'] == 0.0)),:]
answered Sep 22 at 13:00
Charles R
825213
add a comment |
Try to filter your df with loc. It's so powerfull.
The "~" means you want the opposit of your condition.
The ":" means you want to keep all the columns
df = df.loc[~((df['col_1'] == 1.0) & (df['col_2'] == 0.0)),:]
answered Sep 22 at 13:00
Charles R
825213
add a comment |
Try to filter your df with loc. It's so powerfull.
The "~" means you want the opposit of your condition.
The ":" means you want to keep all the columns
df = df.loc[~((df['col_1'] == 1.0) & (df['col_2'] == 0.0)),:]
answered Sep 22 at 13:00
Charles R
825213
Try to filter your df with loc. It's so powerfull.
The "~" means you want the opposit of your condition.
The ":" means you want to keep all the columns
df = df.loc[~((df['col_1'] == 1.0) & (df['col_2'] == 0.0)),:]
answered Sep 22 at 13:00
Charles R
825213
answered Sep 22 at 13:00
Charles R
825213
answered Sep 22 at 13:00
Charles R
825213
answered Sep 22 at 13:00
Charles R
825213
825213
add a comment |
add a comment |
You can use or (|) operator for this ,
Refer this link for it pandas: multiple conditions while indexing data frame - unexpected behavior
i.e dropping rows where both conditions are met
df = df.loc[~((df['col_1']==1) | (df['col_2']==0))]
answered Nov 22 at 18:03
Saurabh
2,08321726
add a comment |
You can use or (|) operator for this ,
Refer this link for it pandas: multiple conditions while indexing data frame - unexpected behavior
i.e dropping rows where both conditions are met
df = df.loc[~((df['col_1']==1) | (df['col_2']==0))]
answered Nov 22 at 18:03
Saurabh
2,08321726
add a comment |
You can use or (|) operator for this ,
Refer this link for it pandas: multiple conditions while indexing data frame - unexpected behavior
i.e dropping rows where both conditions are met
df = df.loc[~((df['col_1']==1) | (df['col_2']==0))]
answered Nov 22 at 18:03
Saurabh
2,08321726
You can use or (|) operator for this ,
Refer this link for it pandas: multiple conditions while indexing data frame - unexpected behavior
i.e dropping rows where both conditions are met
df = df.loc[~((df['col_1']==1) | (df['col_2']==0))]
answered Nov 22 at 18:03
Saurabh
2,08321726
answered Nov 22 at 18:03
Saurabh
2,08321726
answered Nov 22 at 18:03
Saurabh
2,08321726
answered Nov 22 at 18:03
Saurabh
2,08321726
2,08321726
add a comment |
add a comment |
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