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How to drop all rows in pandas dataframe with negative values?


How to drop all rows in pandas dataframe with negative values?

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How to drop all rows in pandas dataframe with negative values?
Tag : python , By : delphiace
Date : January 11 2021, 03:34 PM

will help you Use select_dtypes to get only numeric columns, and keep your code.
df[df.select_dtypes(include=[np.number]).ge(0).all(1)]
df = pd.DataFrame({'col1': [1,2,3,4],
                   'col2': [-1, -2, 3, 4],
                   'col3': ['a','b','c','d'],
                   'col4': [1,2,3,4]})


   col1  col2 col3  col4
0     1    -1    a     1
1     2    -2    b     2
2     3     3    c     3
3     4     4    d     4
    col1    col2    col3    col4
2   3       3       c       3
3   4       4       d       4

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Drop rows of pandas dataframe that don't have finite values in certain variable(s)


Tag : pandas , By : Lars
Date : March 29 2020, 07:55 AM
seems to work fine I cannot see what the built-in function is for the following simple but seemingly common/useful task: Drop rows which have no value for any of my key columns.
adf = adf.dropna(subset=interestingVars, how='all')

python & pandas - Drop rows where column values are index values in another DataFrame


Tag : python , By : snapshooter
Date : March 29 2020, 07:55 AM
Hope this helps The Original DataFrame(df1) looks like:
cols = ['NoDemande','NoUsager']
mask = df1[cols].isin(df2.reset_index()[cols].to_dict('list'))
df1[~mask.all(1)]

Drop or replace values within duplicate rows in pandas dataframe


Tag : python , By : Fernando
Date : March 29 2020, 07:55 AM
I hope this helps you . df.loc[df.duplicated(subset=['B','C']), ['B','C']] = np.nan seems to work for me.
Edited to include @ALollz and @macaw_9227 correction.

how to find rows with both positive and negative values in pandas dataframe


Tag : python , By : Matt Leacock
Date : March 29 2020, 07:55 AM
Hope that helps Edit 2: you may also use transform to avoid set_index/reset_index as follows:
m = df.w_col.lt(0).groupby(df.word_col).transform('nunique').eq(2)
df.loc[m]

Out[2768]:
   e_col  in_col   word_col     w_col
0     31       9  algorithm -0.053538
4     30       8  algorithm  0.043637
m =  df.w_col.lt(0).groupby(df.word_col).nunique().eq(2)
m = df.w_col.lt(0).groupby(df.word_col).unique().str.len().eq(2)
df.set_index('word_col').loc[m].reset_index()

Out[2738]:
    word_col  e_col  in_col     w_col
0  algorithm     31       9 -0.053538
1  algorithm     30       8  0.043637

Drop the rows which contains duplicate values in 2 columns in a pandas dataframe


Tag : python , By : jan
Date : March 29 2020, 07:55 AM
wish of those help Use df.nunique() on axis=1 and filter out rows which return 1:
df[~df.nunique(1).eq(1)]
     A     B
0  cat  fish
2  cat  fish
3  dog   cat
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