Series and DataFrames
Pandas is the go‑to library for data manipulation in Python. It introduces two main structures: Series (1D labeled array) and DataFrame (2D table with rows and columns). Almost all AI data preprocessing starts with Pandas.
A DataFrame is like an Excel spreadsheet or SQL table – rows are observations, columns are features.
Creating a DataFrame
import pandas as pd
# From a dictionary
data = {
'Name': ['Alice', 'Bob', 'Charlie'],
'Age': [25, 30, 35],
'Score': [85, 90, 88]
}
df = pd.DataFrame(data)
print(df) Name Age Score
0 Alice 25 85
1 Bob 30 90
2 Charlie 35 88Series – A Single Column
ages = df['Age']
print(ages) # Series with index 0,1,2
print(ages.mean()) # 30.0Basic DataFrame Inspection
df.head() # first 5 rows
df.info() # data types and missing values
df.describe() # statistics for numeric columnsWhy Pandas for AI?
- Load datasets from CSV, Excel, JSON, SQL.
- Clean data: handle missing values, remove duplicates.
- Filter rows, select columns, group by categories.
- Merge multiple datasets (train + test, features + labels).
Two Minute Drill
- DataFrame = 2D table (rows, columns).
- Series = single column.
df.head()preview,df.info()summary,df.describe()stats.- Pandas is essential for data loading and preprocessing in AI.
Need more clarification?
Drop us an email at career@quipoinfotech.com