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How to delete columns or reorder using column number reference, not column name Python Spark

1

I'm new to python spark having only just started in python, so aplogies if this question is really dim

How do I delete columns or reorder my python spark dataframe using column number refs, not column names? My col names are long and I have a lot of cols, using names is v tedious

I want to turn eg:

 Data = Data.drop("070_thing", "230_anglething", "152_magnetthing", "200_status_thing", "155_thing")

into:

 Data = Data.drop(1, 5, 9, 15, 22)

Also reorder, so instead of:

 df = df.select("id","name","time","city")

I want to put:

 df = df.select(4, 3, 2, 1)

Thanks

python pyspark

share|improve this question

asked 11 hours ago

KieranIngramKieranIngram

276

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Possible duplicate of Pyspark : select specific column with its position
  
  – pault
  5 hours ago
  

  
  
  
  

add a comment | 

1

I'm new to python spark having only just started in python, so aplogies if this question is really dim

How do I delete columns or reorder my python spark dataframe using column number refs, not column names? My col names are long and I have a lot of cols, using names is v tedious

I want to turn eg:

 Data = Data.drop("070_thing", "230_anglething", "152_magnetthing", "200_status_thing", "155_thing")

into:

 Data = Data.drop(1, 5, 9, 15, 22)

Also reorder, so instead of:

 df = df.select("id","name","time","city")

I want to put:

 df = df.select(4, 3, 2, 1)

Thanks

python pyspark

share|improve this question

asked 11 hours ago

KieranIngramKieranIngram

276

• 
  
  
  

  
  

  
  
  
  
  
  

  
  
  Possible duplicate of Pyspark : select specific column with its position
  
  – pault
  5 hours ago
  

  
  
  
  

add a comment | 

I'm new to python spark having only just started in python, so aplogies if this question is really dim

How do I delete columns or reorder my python spark dataframe using column number refs, not column names? My col names are long and I have a lot of cols, using names is v tedious

I want to turn eg:

 Data = Data.drop("070_thing", "230_anglething", "152_magnetthing", "200_status_thing", "155_thing")

into:

 Data = Data.drop(1, 5, 9, 15, 22)

Also reorder, so instead of:

 df = df.select("id","name","time","city")

I want to put:

 df = df.select(4, 3, 2, 1)

Thanks

python pyspark

share|improve this question

asked 11 hours ago

KieranIngramKieranIngram

276

 Data = Data.drop("070_thing", "230_anglething", "152_magnetthing", "200_status_thing", "155_thing")

 Data = Data.drop(1, 5, 9, 15, 22)

 df = df.select("id","name","time","city")

 df = df.select(4, 3, 2, 1)

share|improve this question

asked 11 hours ago

KieranIngramKieranIngram

276

share|improve this question

asked 11 hours ago

KieranIngramKieranIngram

276

asked 11 hours ago

KieranIngramKieranIngram

276

asked 11 hours ago

KieranIngramKieranIngram

276

1 Answer
1

active

oldest

votes

1

you can use df.columns:

columns = Data.columns
Data.select(columns[0], columns[1])

or:

 from operators import itemgetter

 df_getter = lambda cols: list(itemgetter(**cols, columns))
 data.select(*df_getter((1, 2, 3)))

share|improve this answer

answered 10 hours ago

hamza tunahamza tuna

8891512

add a comment | 

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1

you can use df.columns:

columns = Data.columns
Data.select(columns[0], columns[1])

or:

 from operators import itemgetter

 df_getter = lambda cols: list(itemgetter(**cols, columns))
 data.select(*df_getter((1, 2, 3)))

share|improve this answer

answered 10 hours ago

hamza tunahamza tuna

8891512

add a comment | 

1

you can use df.columns:

columns = Data.columns
Data.select(columns[0], columns[1])

or:

 from operators import itemgetter

 df_getter = lambda cols: list(itemgetter(**cols, columns))
 data.select(*df_getter((1, 2, 3)))

share|improve this answer

answered 10 hours ago

hamza tunahamza tuna

8891512

add a comment | 

you can use df.columns:

columns = Data.columns
Data.select(columns[0], columns[1])

or:

 from operators import itemgetter

 df_getter = lambda cols: list(itemgetter(**cols, columns))
 data.select(*df_getter((1, 2, 3)))

share|improve this answer

answered 10 hours ago

hamza tunahamza tuna

8891512

columns = Data.columns
Data.select(columns[0], columns[1])

 from operators import itemgetter

 df_getter = lambda cols: list(itemgetter(**cols, columns))
 data.select(*df_getter((1, 2, 3)))

share|improve this answer

answered 10 hours ago

hamza tunahamza tuna

8891512

answered 10 hours ago

hamza tunahamza tuna

8891512

answered 10 hours ago

hamza tunahamza tuna

8891512