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How to Check Column Nulls and Replace: Pandas

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Here is a post that shows how to count Nulls and replace them with the value you want in the Pandas Dataframe. We have explained the process in two steps - Counting and Replacing the Null values. Count null values (column-wise) in Pandas ## count null values column-wise null_counts = df.isnull(). sum() print(null_counts) ``` Output: ``` Column1    1 Column2    1 Column3    5 dtype: int64 ``` In the above code, we first create a sample Pandas DataFrame `df` with some null values. Then, we use the `isnull()` function to create a DataFrame of the same shape as `df`, where each element is a boolean value indicating whether that element is null or not. Finally, we use the `sum()` function to count the number of null values in each column of the resulting DataFrame. The output shows the count of null values column-wise. to count null values column-wise: ``` df.isnull().sum() ``` ##Code snippet to count null values row-wise: ``` df.isnull().sum(axis=1) ``` In the above code, `df` is the Panda
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Here is a Quick Way to Know Current Working Directory in R

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If R is not finding the file you are trying to read then it may be looking in the wrong folder/directory. If you are using the graphical interface you can change the working directory from the file menu.

List of Files and Current Working Directory

Related: JOBS in R Language

If you are not sure what files are in the current working directory you can use the dir() command to list the files and the getwd() command to determine the current working directory:
> dir()
[1] "fixedWidth.dat" "simple.csv"     "trees91.csv"    "trees91.wk1"[5] "w1.dat"
> getwd()
[1] "/home/black/write/class/stat/stat383-13F/dat"

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