pandas is a Python library for manipulating, cleaning, exploring, and analyzing tabular data through labeled data frames. It supports efficient movement from raw data to summaries, visualizations, and statistical or predictive modeling. section 1.2.1 section 1.4.3
Use pandas to structure an analysis workflow:
- Read a CSV file into a data frame with pd.read_csv(). section 5.2.1
- Inspect dimensions, column types, and missing values with info(), then preview records with head() and tail(). section 5.2.1
- Transform categorical labels with map(), filter observations, and group variables for descriptive statistics. section 5.2.1
- Connect data frames to Matplotlib and seaborn for distributions and correlation heat maps. section 1.2.1 section 5.2.1
Exploring raisin measurements
Import raisin_dataset.csv, inspect its 900 rows and eight columns, encode the Class labels Kecimen and Besni as 0 and 1, and compare feature distributions and grouped statistics before fitting a logistic regression model. section 5.2.1
>>> import pandas as pd
>>> raisins = pd.read_csv('raisin_dataset.csv')
Start a reproducible workflow by importing pandas and loading a CSV file into a DataFrame. section 5.2.1
>>> print(raisins.info()) <class 'pandas.core.frame.DataFrame'> RangeIndex: 900 entries, 0 to 899 Data columns (total 8 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 Area 900 non-null int64 1 MajorAxisLength 900 non-null float64 2 MinorAxisLength 900 non-null float64 3 Eccentricity 900 non-null float64 4 ConvexArea 900 non-null float64 5 Extent 900 non-null float64 6 Perimeter 900 non-null float64 7 Class 900 non-null object dtypes: float64(6), int64(1), object(1) memory usage: 56.4+ KB None
Use info() to validate dimensions, data types, and non-null counts before deeper analysis. section 5.2.1
