How to count outliers for all columns in Python?

Hello Guys, How are you all? Hope You all Are Fine. Today We Are Going To learn about How to count outliers for all columns in Python in Python. So Here I am Explain to you all the possible Methods here.

How to count outliers for all columns in Python?

1. How to count outliers for all columns in Python?

Random data:
np.random.seed(0) df = pd.DataFrame(np.random.randn(100, 5), columns=list('ABCDE')) df.iloc[::10] += np.random.randn() * 2 # this hopefully introduces some outliers df.head()

2. count outliers for all columns in Python

Random data:
np.random.seed(0) df = pd.DataFrame(np.random.randn(100, 5), columns=list('ABCDE')) df.iloc[::10] += np.random.randn() * 2 # this hopefully introduces some outliers df.head()

Method 1

Random data:

np.random.seed(0)
df = pd.DataFrame(np.random.randn(100, 5), columns=list('ABCDE'))
df.iloc[::10] += np.random.randn() * 2  # this hopefully introduces some outliers
Out:
A         B         C         D         E
0  2.529517  1.165622  1.744203  3.006358  2.633023
1 -0.977278  0.950088 -0.151357 -0.103219  0.410599
2  0.144044  1.454274  0.761038  0.121675  0.443863
3  0.333674  1.494079 -0.205158  0.313068 -0.854096
4 -2.552990  0.653619  0.864436 -0.742165  2.269755

Quartile calculations:

Q1 = df.quantile(0.25)
Q3 = df.quantile(0.75)
IQR = Q3 - Q1

And these are the numbers for each column:

((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))).sum()
Out:
A    1
B    0
C    0
D    1
E    2
dtype: int64

In line with seaborn’s calculations:

Note that the part before the sum ((df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))) is a boolean mask so you can use it directly to remove outliers. This sets them to NaN, for example:

mask = (df < (Q1 - 1.5 * IQR)) | (df > (Q3 + 1.5 * IQR))