Data Analysis With Python For Beginners: Learn By Practice

Learn to index and slice numpy arrays, access elements by zero-based indices in 1-D and 2-D arrays, and extract slices using start and end positions and row-column syntax.

Learn to work with 3x3 arrays in Python by solving practice example 3: create two 3x3 arrays, add them element-wise, then multiply a third 3x3 array by five.

Learn to create a 4 by 4 two dimensional array in Python and slice it horizontally to extract the upper or lower half using index positions and the colon operator.

Create a series from a list, define a predefined index, and build a series from a dictionary, then extract values and the index as needed.

Learn to create a data frame from a dictionary, understand its rows and columns with row and column indices, and practice retrieving, reordering, and assigning values in Python.

Create a series of item prices with item names as the index and access prices by indexing, using cell phone, computer, monitor, iPhone with prices 100, 200, 300, 400.

Create a dictionary of consumer items with names and prices, and convert that dictionary into cds. Print the cds and learn to access each item's price by indexing.

Learn by practice how to build a car dataset from a dictionary and convert it into a pandas data frame with columns for brand, model, price, mileage, and year.

Learn to extract car brands from a data frame, retrieve a price by index, set all prices to 100000, and reorder columns so price sits beside brand.

Learn how to apply functions and mapping to data with lambda expressions and the apply method on data frames, transforming data through arithmetic and other operations.

Detect and handle duplicate index values in series and data frames, using is unique method to check duplicates. Prepare data for analysis by computing sum, mean, min, max, and describe.

Create a Python series with product names as the index and prices as values, then sort by index alphabetically and by values descending.

Learn to handle missing data in pandas by dropping rows or columns with missing values and filling remaining gaps with zeros.

Learn to merge datasets by connecting two data frames on keys, including multiple keys, and perform inner, left, right, and outer joins and unions.

Learn to merge on an index, use the index as a key, and concatenate dataframes and series along an axis, including intersections and hierarchical indexing.

Learn how to combine datasets with overlapping indexes using the combine_first approach, prioritizing the first dataset and filling gaps with values from the second.

Demonstrates MoJ operations on data frames by using a common key, performing merges with left on and right on, and finishing with an outer join.

Learn to merge data frames on multiple keys in Python using pandas, creating two frames and performing an outer join on two common columns.

This practice example 24 shows how to merge two pandas series with overlapping indices using the combine_first method, demonstrating concatenation and preserving values from the first series.

Build a 4x4 data frame, use stack to form a hierarchically indexed series, concatenate two series with keys, and unstack to return a data frame.

Master plotting subplots in python by creating figures, configuring line properties such as width and color, and composing a multi-plot layout with grids and diverse plots.

Learn to plot series and data frames in Python using the plot method, create random data, and generate bar and horizontal bar graphs with customization.

Practice example 26 solution demonstrates plotting random x and y values with a labeled x-axis, plotting an array and its squares, and displaying blue dots for points.

Read a csv file in a Jupyter notebook and plot the data on a graph or map. Group by state and sum raised amounts to visualize with bar charts.