MDCIPROPE2026/sesion2_3/pandas_introduction.py

32 lines
1.1 KiB
Python

import pandas as pd # pyright: ignore[reportMissingModuleSource]
import numpy as np
ages = pd.Series([25, 30, 35, 40], name="Ages") # Create a pandas Series from a list
print("ages: \n", ages)
names = pd.Series(["Alice", "Bob", "Charlie", "David"], name="Names") # Create a pandas Series from a list of strings
print("names: \n", names)
"""DataFrame Creation"""
table = pd.concat([names, ages], axis=1) # Concatenate the two Series along the columns to create a DataFrame, axis-1 is for columns
print("table: \n", table)
data = {
"Motor": ["AC_Standard", "Servo", "Stepper", "DC_Brushless"],
"Voltage": [220, 24, 12, 5],
"Current": [5.5, 2.1, 1.8, 0.8],
"Efficiency": [True, True, False, True]
}
df = pd.DataFrame(data) # Create a DataFrame from a dictionary, where keys are column names and values are lists of column data
print("DataFrame: \n", df)
"""Create a DataFrame from a CSV file"""
df = pd.read_csv("DataSources/automobile_parts.csv") # Read a CSV file and create a DataFrame from it, this source can be a SQL data, excel file, json, etc.
print("DataFrame from CSV: \n", df)