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Y1961 Y1962 Y1963 Y1964 Y1965 Region 0 82.567307 83.104757 83.183700 83.030338 82.831958 US 1 2.699372 2.610110 2.587919 2.696451 2.846247 US 2 14.131355 13.690028 13.599516 13.649176 13.649046 US 3 0.048589 0.046982 0.046583 0.046225 0.051750 US 4 0.553377 0.548123 0.582282 0.577811 0.620999 US In the above dataframe, I would like to get average of each row. currently, I am doing this:
df.mean(axis=0) However, this does away with the Region column as well. how can I compute mean and also retain Region column
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The DataFrame. mean() method is used to return the mean of the values for the requested axis. If you apply this method on a series object, then it returns a scalar value, which is the mean value of all the observations in the pandas DataFrame.
mean() function in the Pandas library can be used to find the mean of a series.
Pandas Mean will return the average of your data across a specified axis. If the function is applied to a DataFrame, pandas will return a series with the mean across an axis. If . mean() is applied to a Series, then pandas will return a scalar (single number).
You can specify a new column. You also need to compute the mean along the rows, so use axis=1.
df['mean'] = df.mean(axis=1) >>> df Y1961 Y1962 Y1963 Y1964 Y1965 Region mean 0 82.567307 83.104757 83.183700 83.030338 82.831958 US 82.943612 1 2.699372 2.610110 2.587919 2.696451 2.846247 US 2.688020 2 14.131355 13.690028 13.599516 13.649176 13.649046 US 13.743824 3 0.048589 0.046982 0.046583 0.046225 0.051750 US 0.048026 4 0.553377 0.548123 0.582282 0.577811 0.620999 US 0.576518 If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
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