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Ilia Popov edited this page Aug 30, 2024
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pip install myawesomeeda
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!
![](https://github.com/iliapopov17/MyAwesomeEDA/raw/main/imgs/welcome.gif)
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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