Pandas Certification Guide
Certifications Available
| Cert / Course | Provider | Cost |
|---|
|---------------|----------|------|
| **Kaggle Pandas Course** | Kaggle | Free |
|---|
| DataCamp Data Analyst with Python | DataCamp | ~$300/yr |
| IBM Data Analyst Professional | Coursera | ~$300 |
| Associate Data Analyst in SQL+Python | DataCamp | ~$300/yr |
No standalone Pandas cert — assessed as part of Python/data analyst tracks.
Core Skills & Commands
python
import pandas as pd
# Loading data
df = pd.read_csv("data.csv")
df = pd.read_excel("data.xlsx")
df = pd.read_json("data.json")
# Exploration
df.head() | df.info() | df.describe() | df.shape
df.dtypes | df.isnull().sum() | df.nunique()
# Selection
df["col"] # Series
df[["col1","col2"]] # DataFrame
df.iloc[0:5, 1:3] # by position
df.loc[df["age"] > 30] # by label/condition
df.query("age > 30 and city == 'Mumbai'") # SQL-like
# Cleaning
df.dropna() | df.fillna(0) | df.drop_duplicates()
df["col"] = df["col"].str.strip().str.lower()
df["date"] = pd.to_datetime(df["date"])
df = df.rename(columns={"old": "new"})
# Aggregation
df.groupby("city")["sales"].sum()
df.groupby("city").agg({"sales": "sum", "orders": "count"})
df.pivot_table(values="sales", index="region", columns="product", aggfunc="sum")
# Merging
pd.merge(df1, df2, on="id", how="left")
pd.concat([df1, df2], ignore_index=True)
Study Resources
•Kaggle Pandas Course (free, best starting point)
•Pandas documentation (pandas.pydata.org/docs) — official reference
•Real Python Pandas tutorials — practical examples
•DataCamp Pandas skill track — structured learning path
Revision Notes
PANDAS ESSENTIALS:
Load: read_csv/excel/json | Explore: head/info/describe/dtypes
Select: df["col"] | df.loc[condition] | df.iloc[rows, cols]
Clean: dropna/fillna/drop_duplicates/str.strip/astype
Aggregate: groupby().agg() | pivot_table | value_counts
Merge: pd.merge(df1, df2, on="key", how="left/right/inner/outer")
Apply: df["col"].apply(func) | df.applymap(func)
