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A common task in sentiment analysis is to obtain the count of words within a Pandas data frame cell and create a new column based on that count. How do I do this?
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Count Values in Pandas Dataframe Step 1: . Importing libraries. Step 2: . Step 3: . In this step, we just simply use the .count () function to count all the values of different columns. Step 4: . If we want to count all the values with respect to row then we have to pass axis=1 or ‘columns’. Step ...
You can use the nunique () function to count the number of unique values in a pandas DataFrame. #count unique values in each column df.nunique() #count unique values in each row df.nunique(axis=1)
The Pandas .shape attribute can be used to return a tuple that contains the number of rows and columns, in the following format (rows, columns). If you’re only interested in the number of rows (say, for a condition in a for loop ), you can get the first index of that tuple.
The safest way to determine the number of rows in a dataframe is to count the length of the dataframe’s index. To return the length of the index, write the following code: The Pandas .shape attribute can be used to return a tuple that contains the number of rows and columns, in the following format (rows, columns).
Assuming that a sentence with n words has n-1 spaces in it, there's another solution:
df['new_column'] = df['count_column'].str.count(' ') + 1
This solution is probably faster, because it does not split each string into a list.
If count_column contains empty strings, the result needs to be adjusted (see comment below):
df['new_column'] = np.where(df['count_column'] == '', 0, df['new_column'])
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