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.

Learn to reindex a series and a data frame by setting a new index order, including alphabetical sorting and reshaping numpy-based data.

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.

Explore arithmetic methods with fill values for mismatched indexes in series and data frames. See how fill_value substitutes missing data, preventing undefined results in computations.

Learn arithmetic between a data frame and a series using broadcasting. See how subtraction and addition align by index across rows to update the entire frame.

Learn practice example 9 solution by creating two data streams with shapes 2 by 3 and 3 by 4, adding them, and filling missing values with zero using add.

This practice example demonstrates creating a five-element series and a ten-element dataframe, then performing element-wise subtraction between the series and dataframe to produce the result.

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.

learn how to sort and rank data in Python using series and data frames, sorting by index or by values, with ascending or descending options and handling missing values.

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.

Apply a 15 percent discount to a 3x3 data frame by mapping a discount function (lambda) onto the data frame, demonstrating function application and mapping in Python data analysis.

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.

Demonstrate solving practice example 13 by handling duplicate values in a series and checking for unique index values with index.is_unique, showing how to apply the method to large series.

Create a multi-index series with random values, access an element using hierarchical indexing, and convert the series into a data frame with unstack and stack methods.

Learn how data loading reads tabular data from files and converts it into a data frame, supporting formats like csv, text, and json for downstream processing.

Learn to read data from multiple file formats using Python and pandas, including csv and text with custom delimiters, setting header names, and skipping rows for analysis.

Learn how to read partial data from large files by specifying the number of rows, creating a small data frame, and saving the subset to csv or other text formats.

Learn to create and save a CSP file with random data, read it into a data frame, and read a CSP file with a slash separator to extract two rows.

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 how to merge data frames on multiple keys using Pandas' merge, perform the outer join, and understand how key matching controls the merged results.

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.

Learn to reshape data in a data frame using stack and unstack to create a hierarchical index and handle missing values during reshaping.

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.

Demonstrate creating two data frames, merging on the second frame's index with the first frame's column, and performing self-concatenation to illustrate pandas data frame operations.

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.

Add text to plots by placing labels inside graphs and on the x and y axes, then build a growth chart with random data, revenue and profit lines, and grid.

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.

Create random CDs and random data streams, plot them on a graph with matplotlib and pandas, and generate a series to visualize as a horizontal bar graph.

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.