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Pandas df.describe() is a very useful method to have an overview of your df. However, it describes by columns and I would like to have an overview of the rows instead. Is there any way to make it work "by_row" without transposing the df?
995
We can also use the transpose() method or . T in order to get a transposed version of our dataframe. The output will look something like this (for the first ten rows). If you want to see each columns' name, number of rows, null-value, and data type, use the info() function.
Pandas DataFrame describe() Method The describe() method returns description of the data in the DataFrame. If the DataFrame contains numerical data, the description contains these information for each column: count - The number of not-empty values.
Pandas DataFrame. transpose() is a library function that transpose index and columns. The transpose reflects the DataFrame over its main diagonal by writing rows as columns and vice-versa. Use the T attribute or the transpose() method to swap (= transpose) the rows and columns of DataFrame.
The describe() function computes a summary of statistics pertaining to the DataFrame columns. This function gives the mean, std and IQR values. And, function excludes the character columns and given summary about numeric columns.
Use apply and pass axis=1 to call describe row-wise:
In [274]:
df = pd.DataFrame(np.random.randn(4,5))
df
Out[274]:
0 1 2 3 4
0 0.651863 0.738034 -0.477668 -0.561699 0.047500
1 -1.565093 -0.671551 0.537272 -0.956520 0.301156
2 -0.951549 2.177592 0.059961 -1.631530 -0.620173
3 0.277796 0.169365 1.657189 0.713522 1.649386
In [276]:
df.apply(pd.DataFrame.describe, axis=1)
Out[276]:
count mean std min 25% 50% 75% max
0 5 0.079606 0.609069 -0.561699 -0.477668 0.047500 0.651863 0.738034
1 5 -0.470947 0.878326 -1.565093 -0.956520 -0.671551 0.301156 0.537272
2 5 -0.193140 1.458676 -1.631530 -0.951549 -0.620173 0.059961 2.177592
3 5 0.893451 0.722917 0.169365 0.277796 0.713522 1.649386 1.657189
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