Pandas / Find Values
Find values
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
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()]
statistics = T.to_markdown()
# Unique values
T = pd.DataFrame()
T['unique_sex'] = df['Sex'].unique()
T['value_counts'] = [df['Sex'].value_counts()[0], df['Sex'].value_counts()[1]]
unique_values = T.to_markdown()
# Value counts
T = pd.DataFrame()
T['PClass_value_counts'] = df['PClass'].value_counts()
value_counts = T.to_markdown()
# Missing values
df = df[df['Age'].isnull()]
missing_values = df.head(2).to_markdown()
print("Statistics:"); print(statistics, "\n")
print("Unique values:"); print(unique_values, "\n")
print("Value counts:"); print(value_counts, "\n")
print('Missing values | Age Null:'); print(missing_values, "\n")
"""
Statistics:
| | max | min | avg | sum | cnt |
|---:|------:|------:|-------:|--------:|------:|
| 0 | 71 | 0.17 | 30.398 | 22980.9 | 756 |
Unique values:
| | unique_sex | value_counts |
|---:|:-------------|---------------:|
| 0 | female | 851 |
| 1 | male | 462 |
Value counts:
| | PClass_value_counts |
|:----|----------------------:|
| 3rd | 711 |
| 1st | 322 |
| 2nd | 279 |
| * | 1 |
Missing values | Age Null:
| | Name | PClass | Age | Sex | Survived | SexCode |
|---:|:-----------------------------|:---------|------:|:-------|-----------:|----------:|
| 12 | Aubert, Mrs Leontine Pauline | 1st | nan | female | 1 | 1 |
| 13 | Barkworth, Mr Algernon H | 1st | nan | male | 1 | 0 |
"""
