MLearning
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Pandas
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Find values S
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ML Pandas Find Values
Pandas has multiple built-in methods for descriptive statistics df['Age'].max() df['PClass'].value_counts()
Find values
p42 Pandas has multiple built-in methods for descriptive statistics.
""" Find values and statistics
Pandas has multiple built-in methods for descriptive statistics.
Can be applied to a column or to whole dataframe.
"""
import pandas as pd
import pathlib
DIR = pathlib.Path(__file__).resolve().parent / '../_data/'
df = pd.read_csv(DIR / 'titanic.csv')
# Statistics
T = pd.DataFrame()
T['max'] = [df['Age'].max()]
T['min'] = [df['Age'].min()]
T['avg'] = [df['Age'].mean()]
T['sum'] = [df['Age'].sum()]
T['cnt'] = [df['Age'].count()]
print(T.to_markdown())
# | | max | min | avg | sum | cnt |
# |---:|------:|------:|-------:|--------:|------:|
# | 0 | 71 | 0.17 | 30.398 | 22980.9 | 756 |
# # Unique values
T = pd.DataFrame()
T['unique_sex'] = df['Sex'].unique()
T['value_counts'] = [df['Sex'].value_counts()[0], df['Sex'].value_counts()[1]]
print(T.to_markdown())
# | | unique_sex | value_counts |
# |---:|:-------------|---------------:|
# | 0 | female | 851 |
# | 1 | male | 462 |
# Value counts
T = pd.DataFrame()
T['PClass_value_counts'] = df['PClass'].value_counts()
print(T.to_markdown())
# | | PClass_value_counts |
# |:----|----------------------:|
# | 3rd | 711 |
# | 1st | 322 |
# | 2nd | 279 |
# | * | 1 |
# Missing values
df = df[df['Age'].isnull()]
print('Missing values | Age Null:')
print(df.head(2).to_markdown())
# | | Name | PClass | Age | Sex | Survived | SexCode |
# |---:|:---------------|:---------|------:|:-------|-----------:|----------:|
# | 12 | Aubert, Mrs Leo| 1st | nan | female | 1 | 1 |
# | 13 | Barkworth, Mr A| 1st | nan | male | 1 | 0 |
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Last update: 46 days ago