NumPy, Pandas, & Python for Data Analysis: A Complete Guide

Create NumPy arrays from Python lists to enable efficient numerical operations, including 2d arrays from nested lists and specifying data types with dtype.

Develop skills in indexing numpy arrays to access elements, slicing to extract ranges, and reshaping data into a matrix for analysis.

Explore numpy arrays and master basic operations, including element-wise arithmetic, array comparison, and broadcasting with scalars, through practical examples in Python.

Explore essential numpy mathematical functions, including square root, exponential, sine, and exponentiation, by creating numpy arrays and applying these functions to compute results.

Explore working with multidimensional arrays in numpy, including creating and accessing 2d matrices and 3d arrays, slicing subarrays, and mastering efficient data manipulation in Python.

Explore how to generate random numbers with numpy, producing floating-point values between 0 and 1, random integers in a range, and samples from a standard normal distribution.

learn how to read and write NumPy arrays using text and binary files, including comma-delimited text, save/load text, and binary save/load techniques.

Explore NumPy operations in Python to create arrays, perform vectorized arithmetic, and apply aggregation functions such as sum, mean, and max, plus boolean indexing and broadcasting.

Explore understanding data frames with pandas in Python, learn to create data frames from dictionaries and lists, and manipulate heterogeneous tabular data with labeled columns.

Explore adding, removing, and updating data in pandas data frames, including creating a frame, adding a row, removing a name, and updating a score.

Explore handling missing data with Pandas in Python by identifying, removing, and filling missing values in a data frame, using methods like isna and dropna to clean real-world datasets.

Learn to filter, sort, and group data using Pandas in Python, including filtering by age, sorting by salary, and computing average salaries by department.

Learn to work with time series data in pandas by converting date columns to datetime, indexing by date, and resampling daily data to monthly means.

Convert string data to date time in pandas for analysis. Extract year, month, and day from datetime objects for time series operations.

Explore time series analysis and manipulation with pandas, covering resampling to change frequency (daily to monthly), shifting for alignment, and rolling statistics to compute rolling mean.

Identify and handle missing data in pandas by locating gaps with isna and filling them with a specific value or the column mean.

Identify and remove duplicates in pandas data frames using drop_duplicates, first by a single column (name) and then across all columns, leveraging numpy and pandas for clean data sets.

Learn data transformation and normalization with pandas and numpy, including log transformation and min-max normalization to prepare features for analysis.

Learn to merge, join, and concatenate dataframes in pandas using common columns and the index, with practical examples of pd.merge and pd.concat for data analysis.

Reshape and pivot data with pandas in Python to convert wide data into long format using melt. Build and analyze pivot tables to compare student scores across math and science.

Apply element-wise and column-wise functions to data frames with pandas using apply, applymap, and lambda. Learn to transform data and compute column sums.

Explore pandas data visualization to quickly explore data, view distributions with histograms, box plots, scatter plots, and pair plots, and create interactive plots with matplotlib and seaborn.

Learn how pandas simplifies data visualization with basic plots, including line plots for temperature trends, bar plots for product sales, and histograms for age distribution, using matplotlib.

Learn to customize pandas plots by adding titles, labels, and colors to line and bar charts, using daily sales data to improve clarity and visual appeal.

Create complex visualizations with Pandas in Python, including histograms, scatter plots, and box plots, to analyze data distribution, relationships, and summaries.

Explore memory optimization techniques in Pandas for large data sets by converting data types to category and 32-bit integers to reduce memory usage.

Leverage vectorized operations in Pandas and NumPy to perform element-wise addition on large dataframes, then speed up computation with parallel processing using joblib to compute means across chunks.

Learn to prepare data for machine learning using pandas, including data cleaning, filling missing values with median and mean, and outlier removal; create a BMI feature from height and weight.

Learn to convert strings to date time in pandas, create date time objects, and extract year, month, and day from a date time column for time series analysis.

Explore basic machine learning with Pandas and NumPy, implementing linear regression and logistic regression using scikit-learn, fitting models, predicting outcomes, and interpreting coefficients and intercepts.