The data on 'A' is a Pandas Series. I would like to compute the average and standard deviation for the data in 'A' (there are several records for A) for example: (mean=(2.0+3.0+4.0+5.0+6.0+7.0+11.0+90.0+43.0+87.0)/len(A)=25.8) More on stackoverflow.com

I am trying to calculate the standard deviation of the mean for a given item. I am having issues using the specific item because the data frame is not recognizing the attribute. The output now is g... More on stackoverflow.com

Woah, that took me a little bit to get... But I get it! haha The problem is that you are using the Average column when calculating 'Std Dev'. This adds 1 to the n in the denominator while not modifying the numerator. To compensate for that, you need to subtract an extra 1 from the denominator (using ddof=2). To alleviate, you can calculate average and stddev separately and then append them to the dataframe. avg = ... # calculate avg here std = ... # calculate std here df['Average'] = avg df['Std Dev'] = std If you get interested, the "clean" way of doing this would be melting the dataframe and then calculating the mean and stddev with a groupby, but that may be a bit overkill (since melt painful to learn). Cheers! More on reddit.com

You can't pass precomputed errors, but you can plot the error bars separately using matplotlib's plt.errorbar. This should help More on reddit.com