How do I get a summary count of missing/NaN data by column in ‘pandas’?
Both describe and info report the count of non-missing values. In [1]: df = DataFrame(np.random.randn(10,2)) In [2]: df.iloc[3:6,0] = np.nan In [3]: df Out[3]: 0 1 0 -0.560342 1.862640 1 -1.237742 0.596384 2 0.603539 -1.561594 3 NaN 3.018954 4 NaN -0.046759 5 NaN 0.480158 6 0.113200 -0.911159 7 0.990895 0.612990 8 0.668534 -0.701769 9 -0.607247 … Read more