## Drop rows with all zeros in pandas data frame

It turns out this can be nicely expressed in a vectorized fashion:

`> df = pd.DataFrame({'a':[0,0,1,1], 'b':[0,1,0,1]})`

> df = df[(df.T != 0).any()]

> df

a b

1 0 1

2 1 0

3 1 1

## How to remove rows from a DataFrame where some columns only have zero values

try this,

`df[~df[list('cdef')].eq(0).all(axis = 1)]`

` a b c d e f`

0 1 2 3 4 5 6

1 11 22 33 44 55 66

## Drop row in pandas dataframe if any value in the row equals zero

i think the easiest way is looking at rows where all values are not equal to 0:

`df[(df != 0).all(1)]`

## How to remove rows with any zero value

There are a few different ways of doing this. I prefer using `apply`

, since it's easily extendable:

`##Generate some data`

dd = data.frame(a = 1:4, b= 1:0, c=0:3)

##Go through each row and determine if a value is zero

row_sub = apply(dd, 1, function(row) all(row !=0 ))

##Subset as usual

dd[row_sub,]

## remove R Dataframe rows based on zero values in one column

Just subset the data frame based on the value in the `No_of_Mails`

column:

`df[df$No_of_Mails != 0, ]`

#### Demo

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