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select columns based on multiple conditions in Pandas
Does Python have a ternary conditional operator?How to join (merge) data frames (inner, outer, left, right)Add one row to pandas DataFrameSelecting multiple columns in a pandas dataframeRenaming columns in pandasDelete column from pandas DataFrame by column name“Large data” work flows using pandasHow to iterate over rows in a DataFrame in Pandas?Select rows from a DataFrame based on values in a column in pandasGet list from pandas DataFrame column headers
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How to translate SQL statement to Pandas:
select PolicyNumber,
Coverage
from ClaimsData
where AccidentState = 'AZ' and Coverage = 'Liability'
I know how select columns for single condition (AccidentState = 'AZ') but how can I add another condition?
ClaimsData[["PolicyNumber","Coverage","AccidentState"]] [ClaimsData["AccidentState"]=="AZ"] # and here I need to add another condition
python pandas dataframe
asked Mar 8 at 6:06
OlegOleg
1,45221953
add a comment |
How to translate SQL statement to Pandas:
select PolicyNumber,
Coverage
from ClaimsData
where AccidentState = 'AZ' and Coverage = 'Liability'
I know how select columns for single condition (AccidentState = 'AZ') but how can I add another condition?
ClaimsData[["PolicyNumber","Coverage","AccidentState"]] [ClaimsData["AccidentState"]=="AZ"] # and here I need to add another condition
python pandas dataframe
asked Mar 8 at 6:06
OlegOleg
1,45221953
add a comment |
How to translate SQL statement to Pandas:
select PolicyNumber,
Coverage
from ClaimsData
where AccidentState = 'AZ' and Coverage = 'Liability'
I know how select columns for single condition (AccidentState = 'AZ') but how can I add another condition?
ClaimsData[["PolicyNumber","Coverage","AccidentState"]] [ClaimsData["AccidentState"]=="AZ"] # and here I need to add another condition
python pandas dataframe
asked Mar 8 at 6:06
OlegOleg
1,45221953
How to translate SQL statement to Pandas:
select PolicyNumber,
Coverage
from ClaimsData
where AccidentState = 'AZ' and Coverage = 'Liability'
I know how select columns for single condition (AccidentState = 'AZ') but how can I add another condition?
ClaimsData[["PolicyNumber","Coverage","AccidentState"]] [ClaimsData["AccidentState"]=="AZ"] # and here I need to add another condition
python pandas dataframe
python pandas dataframe
asked Mar 8 at 6:06
OlegOleg
1,45221953
asked Mar 8 at 6:06
OlegOleg
1,45221953
asked Mar 8 at 6:06
OlegOleg
1,45221953
asked Mar 8 at 6:06
OlegOleg
1,45221953
asked Mar 8 at 6:06
OlegOleg
1,45221953
1,45221953
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
You can chain conditions by & for bitwise AND and filter by DataFrame.loc rows by conditions (boolean indexing) and columns by columns names in list:
mask = (ClaimsData["AccidentState"]=="AZ") & (ClaimsData["Coverage"]=="Liability")
df = ClaimsData.loc[mask, ["PolicyNumber","Coverage"]]
Like @Sandeep Kadapa pointed in comments there is alternative with DataFrame.query:
q = "AccidentState == 'AZ' & Coverage == 'Liability'"
df = ClaimsData.query(q)[["PolicyNumber","Coverage"]]
answered Mar 8 at 6:07
jezraeljezrael
356k26320396
Thanks. So there is no way first choose the columns I need and then specify conditions? I came from SQL background and that would be so much easier
– Oleg
Mar 8 at 6:11
@Oleg Checkquery, this would help.
– Sandeep Kadapa
Mar 8 at 6:12
@Oleg - It is in one step - filtering by rows, and filtering by columns.
– jezrael
Mar 8 at 6:12
add a comment |
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1 Answer
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oldest
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1 Answer
1
active
oldest
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oldest
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oldest
votes
You can chain conditions by & for bitwise AND and filter by DataFrame.loc rows by conditions (boolean indexing) and columns by columns names in list:
mask = (ClaimsData["AccidentState"]=="AZ") & (ClaimsData["Coverage"]=="Liability")
df = ClaimsData.loc[mask, ["PolicyNumber","Coverage"]]
Like @Sandeep Kadapa pointed in comments there is alternative with DataFrame.query:
q = "AccidentState == 'AZ' & Coverage == 'Liability'"
df = ClaimsData.query(q)[["PolicyNumber","Coverage"]]
answered Mar 8 at 6:07
jezraeljezrael
356k26320396
Thanks. So there is no way first choose the columns I need and then specify conditions? I came from SQL background and that would be so much easier
– Oleg
Mar 8 at 6:11
@Oleg Checkquery, this would help.
– Sandeep Kadapa
Mar 8 at 6:12
@Oleg - It is in one step - filtering by rows, and filtering by columns.
– jezrael
Mar 8 at 6:12
add a comment |
You can chain conditions by & for bitwise AND and filter by DataFrame.loc rows by conditions (boolean indexing) and columns by columns names in list:
mask = (ClaimsData["AccidentState"]=="AZ") & (ClaimsData["Coverage"]=="Liability")
df = ClaimsData.loc[mask, ["PolicyNumber","Coverage"]]
Like @Sandeep Kadapa pointed in comments there is alternative with DataFrame.query:
q = "AccidentState == 'AZ' & Coverage == 'Liability'"
df = ClaimsData.query(q)[["PolicyNumber","Coverage"]]
answered Mar 8 at 6:07
jezraeljezrael
356k26320396
Thanks. So there is no way first choose the columns I need and then specify conditions? I came from SQL background and that would be so much easier
– Oleg
Mar 8 at 6:11
@Oleg Checkquery, this would help.
– Sandeep Kadapa
Mar 8 at 6:12
@Oleg - It is in one step - filtering by rows, and filtering by columns.
– jezrael
Mar 8 at 6:12
add a comment |
You can chain conditions by & for bitwise AND and filter by DataFrame.loc rows by conditions (boolean indexing) and columns by columns names in list:
mask = (ClaimsData["AccidentState"]=="AZ") & (ClaimsData["Coverage"]=="Liability")
df = ClaimsData.loc[mask, ["PolicyNumber","Coverage"]]
Like @Sandeep Kadapa pointed in comments there is alternative with DataFrame.query:
q = "AccidentState == 'AZ' & Coverage == 'Liability'"
df = ClaimsData.query(q)[["PolicyNumber","Coverage"]]
answered Mar 8 at 6:07
jezraeljezrael
356k26320396
You can chain conditions by & for bitwise AND and filter by DataFrame.loc rows by conditions (boolean indexing) and columns by columns names in list:
mask = (ClaimsData["AccidentState"]=="AZ") & (ClaimsData["Coverage"]=="Liability")
df = ClaimsData.loc[mask, ["PolicyNumber","Coverage"]]
Like @Sandeep Kadapa pointed in comments there is alternative with DataFrame.query:
q = "AccidentState == 'AZ' & Coverage == 'Liability'"
df = ClaimsData.query(q)[["PolicyNumber","Coverage"]]
answered Mar 8 at 6:07
jezraeljezrael
356k26320396
edited Mar 8 at 6:14
answered Mar 8 at 6:07
jezraeljezrael
356k26320396
answered Mar 8 at 6:07
jezraeljezrael
356k26320396
answered Mar 8 at 6:07
jezraeljezrael
356k26320396
356k26320396
Thanks. So there is no way first choose the columns I need and then specify conditions? I came from SQL background and that would be so much easier
– Oleg
Mar 8 at 6:11
@Oleg Checkquery, this would help.
– Sandeep Kadapa
Mar 8 at 6:12
@Oleg - It is in one step - filtering by rows, and filtering by columns.
– jezrael
Mar 8 at 6:12
add a comment |
Thanks. So there is no way first choose the columns I need and then specify conditions? I came from SQL background and that would be so much easier
– Oleg
Mar 8 at 6:11
@Oleg Checkquery, this would help.
– Sandeep Kadapa
Mar 8 at 6:12
@Oleg - It is in one step - filtering by rows, and filtering by columns.
– jezrael
Mar 8 at 6:12
Thanks. So there is no way first choose the columns I need and then specify conditions? I came from SQL background and that would be so much easier
– Oleg
Mar 8 at 6:11
Thanks. So there is no way first choose the columns I need and then specify conditions? I came from SQL background and that would be so much easier
– Oleg
Mar 8 at 6:11
@Oleg Check
query, this would help.– Sandeep Kadapa
Mar 8 at 6:12
@Oleg Check
query, this would help.– Sandeep Kadapa
Mar 8 at 6:12
@Oleg - It is in one step - filtering by rows, and filtering by columns.
– jezrael
Mar 8 at 6:12
@Oleg - It is in one step - filtering by rows, and filtering by columns.
– jezrael
Mar 8 at 6:12
add a comment |
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