๐ผ Pandas DataFrames
Pandas DataFrame is a primary two-dimensional tabular data structure in the pandas Python library. They allow you to store and manipulate tabular data efficiently.
๐ก Quick Tip:
Mastering this concept will significantly boost your Python data science skills!
๐ป Code Example:
import pandas as pd import numpy as np # 1. Create DataFrame from dict data = { "username": ["santoshtvk", "dhruv", "tvk", "alice", "bob"], "course" : ["Python", "AI", "Python", "DevOps", "AI"], "score" : [95, 88, 72, 65, 91], "premium" : [True, True, False, False, True], "joined" : pd.to_datetime(["2024-01-15","2024-02-20","2024-03-01","2024-03-15","2024-04-01"]), } df = pd.DataFrame(data) # 2. Basic exploration print("Shape:", df.shape) print("\nInfo:") df.info() print("\nDescribe:\n", df.describe()) # 3. Selection & filtering print("\nPremium users:\n", df[df["premium"] == True][["username","score"]]) print("\nScore > 80:\n", df.loc[df["score"] > 80, ["username","course","score"]]) # 4. GroupBy aggregation stats = df.groupby("course").agg( count=("username", "count"), avg_score=("score", "mean"), max_score=("score", "max"), ).round(1) print("\nCourse stats:\n", stats) # 5. New columns df["grade"] = pd.cut(df["score"], bins=[0,60,75,90,100], labels=["C","B","A","A+"]) df["days_active"] = (pd.Timestamp.now() - df["joined"]).dt.days # 6. Merge (join) plans = pd.DataFrame({ "username": ["santoshtvk", "dhruv"], "plan" : ["Annual", "Monthly"], }) merged = df.merge(plans, on="username", how="left") print("\nMerged sample:\n", merged[["username","score","grade","plan"]].head()) # 7. Pivot table pivot = df.pivot_table(values="score", index="course", columns="premium", aggfunc="mean").round(1) print("\nPivot (score by course x premium):\n", pivot) <!-- SECTION:
Keep exploring and happy coding! ๐ป
