Calculate the mean, median, and mode in pandas Python

In this video we go over how to calculate the measures of central tendency (i.e., mean, median, and mode) for an entire DataFrame and a Series.

We also discuss some optimization techniques to improve the speed of your calculation.

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0:00 Intro
0:17 Data
0:39 Mean for DF
1:00 Median for DF
1:07 Mode for DF
1:24 For Single Column
2:00 Optimization

[lyguoguo]

Thanks you for your video which is very helpful for me. In the process of processing the data, I found some special examples for computing median. When the data column dtype is "object", the speed of np.median(df_col.values) is slower than df_col.median(). If possible, it is best to convert data from Object type to float or int before computing median.
Here is the code:
da1 = pd.DataFrame({"col_1":np.random.randint(0, 10, 10**6)})
da1["col_1"] = da1["col_1"].astype(object)
%timeit da1["col_1"].median()
%timeit

[abdullahfaizal]

Hi bro, Thanks for your video. BTW, I run the code %timeit df['col1'].median()
%timeit np.median(df['col1'].values). for me, I got an output this: 69.9 µs ± 3.22 µs per loop (mean ± std. dev. of 7 runs, 10, 000 loops each)
112 µs ± 4.77 µs per loop (mean ± std. dev. of 7 runs, 10, 000 loops each). which means the first code is faster than the second one. can you clarify me. TIA.