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A function to fill in a column with NA of the same type

By : user2175296
Date : October 15 2020, 08:10 PM
it fixes the issue I have a data frame with many columns of different types. I would like to replace each column with NA of the corresponding class. , You can use this "trick" :
code :
df[1:nrow(df),1] <- NA
df[1:nrow(df),2] <- NA
rowIdxs <- 1:nrow(df)
df[rowIdxs ,1] <- NA
df[rowIdxs ,2] <- NA
df[rowIdxs ,3] <- NA
df[TRUE, 1] <- NA
df[TRUE, 2] <- NA
df[TRUE, 1:3] <- NA
# or
df[1:nrow(df), 1:3] <- NA

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excel vba ,to insert a new column to the length of the column next to it and type Ck DUP in column c and fill column B

By : iris
Date : March 29 2020, 07:55 AM
it should still fix some issue I have tried to insert an new column and then insert a value ck dup and use fill down to fill in the column.The number of rows should be equal to the number of rows in Previous column. , Try the below:
code :
Sub InsertColumn()
Dim lLastRow As Long
    lLastRow = Range("B" & Application.Rows.Count).End(xlUp).Row
    Columns("C:C").Insert Shift:=xlToRight, CopyOrigin:=xlFormatFromLeftOrAbove
    If lLastRow < 2 Then
        MsgBox "Last Row is less then 2. Nothing to fill"
        Range("C2:C" & lLastRow) = "ck dup"
    End If
End Sub

How to fill in a column with a function of another?

By : Puru S Badukale
Date : March 29 2020, 07:55 AM
To fix this issue You can use pd.Series.fillna to fill in missing values:
code :
df['day_charge'] = df['day_charge'].fillna(df['day_mins'] * 0.17)

Spark dataframe na.fill boolean column type

By : user1394243
Date : March 29 2020, 07:55 AM
wish of those help na.fill Boolean type is added at version 2.3.0 and previous versions don't support filling Boolean type column. See the API specs here.

Is there an R function to fill NA value of a column in a specific way?

By : garzooma
Date : March 29 2020, 07:55 AM
I wish this help you So I have a data frame like this : , Not a direct function but something like this would work
code :
#Get indices for NA non-zero values
inds1 <- is.na(df$Result) & df$Value2 != 0
#Get indices for NA zero values
inds2 <- is.na(df$Result) & df$Value2 == 0

#Replace them
df$Result[inds1] <- df$Value1[inds1]/df$Value2[inds1]
df$Result[inds2] <- 0

substring function return column type instead of a value. Is there a way to fetch a value out of column type in pyspark

By : user3150147
Date : March 29 2020, 07:55 AM
may help you . I am comparing a condition with pyspark join in my application by using substring function. This function is returning a column type instead of a value. , creating a sample dataframes to join
code :
    list1 = [('ABC','abcdefghijklmno'),('XYZ','abcdefghijklmno'),('DEF','abcdefghijklabc')]
    df1=spark.createDataFrame(list1, ['col1', 'col2'])

    list2 = [(1,'mno'),(2,'mno'),(3,'abc')]
    df2=spark.createDataFrame(list2, ['col1', 'col2'])

import pyspark.sql.functions as f
cond=f.substring(df1['col2'], -3, 3)==df2['col2']

>>> newdf.show()
|col1|           col2|col1|col2|
| ABC|abcdefghijklmno|   1| mno|
| ABC|abcdefghijklmno|   2| mno|
| XYZ|abcdefghijklmno|   1| mno|
| XYZ|abcdefghijklmno|   2| mno|
| DEF|abcdefghijklabc|   3| abc|
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