Pandas Tutorial for Data Science
📘 Data Science
👁 90 views
📅 Nov 14, 2025
⏱ Estimated reading time: 1 min
Introduction
Pandas is the most important library for data manipulation and analysis. It provides two key data structures: Series and DataFrame.
1. Creating a Series
import pandas as pd
s = pd.Series([10, 20, 30])
print(s)
2. Creating a DataFrame
df = pd.DataFrame({
"Name": ["John", "Mary"],
"Age": [28, 34]
})
print(df)
3. Reading Data
df = pd.read_csv("data.csv")
df.head()
4. Selecting Columns
df["Age"]
df[["Name", "Age"]]
5. Filtering Data
df[df["Age"] > 30]
6. Handling Missing Values
df.isnull().sum()
df.fillna(0)
df.dropna()
7. Sorting Data
df.sort_values("Age", ascending=False)
8. Grouping Data
df.groupby("Department")["Salary"].mean()
9. Merging DataFrames
merged = pd.merge(df1, df2, on="id")
Conclusion
Pandas is the heart of data analysis, making data cleaning and transformation fast and efficient.
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