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I performed a measurement where I changed a parameter and measured a physical quantity. I performed multiple measurements and saved the data to a pandas dataframe. The result looks something like this:

   parameter  measured_value
0         10            1.10
1         20            1.21
2         30            1.29
3         40            1.42
4         50            1.54
5         10            1.14
6         20            1.22
7         30            1.32
8         40            1.41
9         50            1.52

In that example I repeated the measurement twice and varied the parameter from 10 to 50 in steps of 10. Is there a way to to average the measured values, such that I get the following result:

   parameter  mean_measured_value
0         10            1.10
1         20            1.20
2         30            1.30
3         40            1.40
4         50            1.50

I analyze my data typically with matlab. Basically, I could use numpy to do data analysis like matlab, but this looks quiet unelegant:

meas_value = np.asarray(df['measured_value'])
mean_meas_value = np.mean(np.reshape(meas_value, (5,2)), axis=1)

Is there an elegant way with pandas?

J. Doist
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1 Answers1

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If I understood you right:

meas_value = df.groupby('parameter').sum()
gtomer
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