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MyAwesomeEDA Usage Guide

Installation

pip install myawesomeeda

Usage Guide

Input

import pandas as pd
from my_awesome_eda import run_eda

df = pd.read_csv('data/titanic.csv')

run_eda(df)

"Unique threshold for categorical features: "
# you need to manually input a number - threshold to categorize features to categorical features

Output

Welcome to the Awesome EDA Module!

Number of observations (rows):
891
Number of parameters (columns):
12

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Data types of each column:
PassengerId      int64
Survived         int64
Pclass           int64
Name            object
Sex             object
Age            float64
SibSp            int64
Parch            int64
Ticket          object
Fare           float64
Cabin           object
Embarked        object

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Numerical features:
['PassengerId', 'Age', 'SibSp', 'Parch', 'Fare']

String features:
['Name', 'Ticket', 'Cabin']

Categorical features:
['Survived', 'Pclass', 'Sex', 'Embarked']

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Counts and frequencies for Survived:
0: Counts=549, Frequencies=61.62%
1: Counts=342, Frequencies=38.38%

Counts and frequencies for Pclass:
3: Counts=491, Frequencies=55.11%
1: Counts=216, Frequencies=24.24%
2: Counts=184, Frequencies=20.65%

Counts and frequencies for Sex:
male: Counts=577, Frequencies=64.76%
female: Counts=314, Frequencies=35.24%

Counts and frequencies for Embarked:
S: Counts=644, Frequencies=72.28%
C: Counts=168, Frequencies=18.86%
Q: Counts=77, Frequencies=8.64%

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Summary statistics for numerical features:
       PassengerId         Age       SibSp       Parch        Fare
count   891.000000  714.000000  891.000000  891.000000  891.000000
mean    446.000000   29.699118    0.523008    0.381594   32.204208
std     257.353842   14.526497    1.102743    0.806057   49.693429
min       1.000000    0.420000    0.000000    0.000000    0.000000
25%     223.500000   20.125000    0.000000    0.000000    7.910400
50%     446.000000   28.000000    0.000000    0.000000   14.454200
75%     668.500000   38.000000    1.000000    0.000000   31.000000
max     891.000000   80.000000    8.000000    6.000000  512.329200

Outliers count for numerical features:
Outliers count for PassengerId: 0
Outliers count for Age: 11
Outliers count for SibSp: 46
Outliers count for Parch: 213
Outliers count for Fare: 116

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Total missing values:
866
Rows with missing values:
708
Columns with missing values:
['Age', 'Cabin', 'Embarked']

Number of duplicate rows:
0

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