Name: Data analysis and visualization in Python with Pandas
Rating: 4.8 (49 reviews)

Udemy Business

Teach on Udemy

Turn what you know into an opportunity and reach millions around the world.

Learn More

Your cart is empty.

Keep shopping

Created byArdian Grezda

Last updated 3/2024

English

What you'll learn

Basics in pandas library
File reading and writing
Data visualization using matplotlib
Data wrangling
Data agreggation
Time series

Course content

7 sections • 92 lectures • 10h 39m total length

Series part 19:21
Series Part 28:40
Explore basic series arithmetic, creating series from dictionaries, and index handling in pandas, including isnull checks, null value detection, and named series and index; plus adding series with aligned indexes.
DataFrame part 18:08
DataFrame part 26:31
Modify a data frame by assigning to a column and using a series, create new columns, delete columns, and build data frames from dictionaries for Italy and Germany across years.
Index object2:20
Reindexing10:55
Deleting data from the axis6:41
Demonstrate deleting data from the axis in Pandas with the drop method on series and dataframe, removing indexes and columns using axis controls.
Indexing, selection and filtering Part 17:32
Explore indexing, selection, and filtering in pandas series, including label and position retrieval, slicing, boolean masking, and updating values such as B and C, similar to numpy.
Indexing, selection and filtering Part 29:34
Explore indexing, selection, and filtering in a data frame, including selecting columns and rows with lists, boolean masks, and conditional updates.
Indexing, selection and filtering Part 36:45
Arithmetics with Series and DataFrames9:21
explore arithmetics with series and data frames by adding pandas objects and aligning by index and columns. see how sums handle missing indices or columns with not a number.
Functions and mapping7:32
Sorting in pandas9:55
Sort data in pandas by index and by columns using sort_index, and order values with sort_values. Learn to sort by a specific column (axis=1) or by index across rows.
Indexes with duplicate values2:48
Explore how pandas handles indexes with duplicate values by inspecting a series and a data frame, and verify index uniqueness using is unique while selecting entries by index label.
Class work no 11:59
Solution to class work no 17:24
Demonstrate a pandas workflow by building a country population dataframe, adding female and male columns, computing 70% of male, calculating percent of male, and removing the dump column.

Statistical description methods in pandas part 19:22
Statistical description methods in pandas part 24:17
Describe how pandas handles non-numerical data, showing count, unique, top, and freq, and summarize methods like min, max, argmin, argmax, idxmin, idxmax, sum, mean, median, var, std, diff, and pct_change.
Unique Values and Value counting part 13:30
Unique Values and Value counting part 27:46
Explore using the isin method to filter unique values and build a boolean mask, then apply pandas value_counts across Q1–Q3 with zeros for non-numeric values.
Manipulation of missing data2:37
Learn to manipulate missing data in pandas by filtering with dropna, using a series containing none values, and observing how dropna drops missing entries while preserving valid data.
Filtering missing data8:21
Filling missing data7:17
Learn how to fill missing data in pandas using fillna with scalar values, per-column fills via a dictionary, and mean (average) imputation for a series in a data frame.
Hierachical indexing Part 19:56
Explore hierarchical indexing in pandas, using multi-level indexes and unstack to transform a multi-index series into a data frame, with practical examples of indexing, selecting, and unstacking.
Hierachical indexing Part 28:56
Using dataframe columns4:51
Class work no 22:33
Create a pandas data frame from cattle, chicken, and sheep, indexed by country; use lambda to find min and max, then compute sum and average; sort by index and sheep.
Solution to class work no 214:54

Reading and writing data from file Part 18:38
Reading and writing data from file Part 28:24
Learn to read and write data from files with pandas, including multi-index creation using a list of index columns, handling missing values with na_values, and skipping rows with read_csv.
Partial reading of text files11:00
Learn to perform partial reading of large text files with pandas by reading in chunks using read_csv and chunk size, then compute value counts by key.
Writing data out to text format5:17
Reading Excel files2:43
Read excel files into pandas data frames using xlrd and openpyxl, load a file, parse sheet one, and produce a data frame with columns A, B, C, D, and message.
json data6:20
Learn json data concepts and handling in python by using the json library to load and dump data, and pandas to build dataframes from json with selected fields.
Class work no 31:33
Solution to class work no 312:22
Learn to load three year data frames with pandas read_csv, filter by gender, sum totals, and count names like Grace and Alice.

Data vizualization Line drawing11:47
Scatter graph4:38
Bar graph6:10
Pie graph5:37
Explore creating a pie graph with matplotlib using a one-dimensional data array, customizing with labels, explode, colors from seaborn palettes, auto percent, and an optional shadow.
Advanced drawing 2d7:46
Title tick and label positioning7:56
Legend positioning4:18
Line drawing in pandas8:02
Bar drawing in pandas7:30
Scatter graph in pandas5:28
Class work no 41:44
Read macro data and marker data csv files into data frames, create frames a, b, and c with year, real gdp, and real cons, and plot the graph for 1960–1963.
Solution to class work no 48:03
Read macro data from csv files with pandas to build data frames. Visualize real GDP and related metrics using line plots and horizontal bar charts.

Data wrangling1:01
Master data wrangling in Python with Pandas by reading, cleaning, transforming, and rearranging data, and learn how to combine datasets with merge and concat operations.
Merging DataFrames part 110:46
Merge data frames using pandas' merge method to link rows by keys. Learn inner, left, right, and outer joins and how to use on, left_on, and right_on for column names.
Merging DataFrames part 211:28
Merging index objects12:27
Explore merging dataframes by index and by column in pandas, using left_on and right_index, including multi-index joins and the outer join to align all indexes.
Concatenation in pandas Part 14:43
Concatenation in pandas Part 28:36
Explore how to concatenate data frames in pandas using pd.concat, control join behavior with axis and inner join, and build hierarchical indexes with keys.
DataFrame Rearrangement4:56
Learn how hierarchical indexing enables data frame rearrangement in pandas, using stack and unstuck to rotate between rows and columns.
Removing duplicate data3:34
Data transformation using function or mapping6:10
Replacing values in pandas4:59
Learn to replace values in pandas series using the replace method, including replacing with numpy NaN, handling multiple replacements, and using a dict to map old values to new ones.
Renaming indexes in pandas5:49
Class work no 52:36
Solution to class work no 59:58

Groupby mechanics part 18:25
Groupby mechanics part 27:29
Group iteration2:12
Column selection4:38
Grouping with dictionary and Series4:39
Group a dataframe by a dictionary mapping columns to red and blue, then sum values to compare red (a, b) and blue (c, d) groups.
Grouping with functions5:31
Explore grouping with functions in Pandas, using len to group by index length and compute sums or minimum. Build multi index results; relate functions to lists, arrays, dictionaries, and series.
Data aggregation8:40
Grouping by columns7:04
Multiple functions application3:34
General form of operation split apply combine9:24
Pivot tables7:09
Explore pivot tables in pandas to summarize and aggregate data by multiple keys, using pivot_table and group by, with margins and aggregate functions like count and sum.
Class work no 61:38
Solution to class work no 69:37

Time series introduction1:05
Date and time data types7:51
Converting from string to date11:44
Time series basics3:53
Indexing and selection11:40
Time series with double indexes5:01
Resample conversion4:05
Date range generation9:38
Learn to generate date time indexes with pandas date_range, using start, end, or periods, and apply daily (D) or last business day (BM) frequencies.
Frequencies and date shift6:04
Data replacement (before and after)7:44
Periods and arithemtics9:36
Period conversion6:18
Learn to convert period index objects to different frequencies in pandas, transitioning from annual December periods to monthly start and end indexes, and creating series with month-end and month-start indexes.
Conversion from timestamps to periods8:54
Creation of PeriodIndex from arrays7:45
Resampling and frequency conversion Downsampling13:18
Explore resampling and frequency conversion in pandas, focusing on downsampling high-frequency data to lower frequencies and upsampling to higher frequencies, with mean and sum aggregations.
Upsampling5:21
Drawing time series7:59
Draw time series in pandas by loading stock data from csv and parsing dates; set a date index, select AAPL, MSFT, XOM, SP, fill missing values forward, and plot trends.
Drawing time series example9:57

Requirements

The student should have basic understanding of Python programming language

Description

The course title is “Data analysis and visualization using Python” and it is using the pandas library.

It is divided into 7 chapters.

Chapter 1 talk about creation of pandas objects such as: Series, DataFrame, Index. This chapter includes basic arithmetic with pandas object. Also it describes other operations with pandas object such as: reindexing, deleting data from axis, filtering, indexing and sorting.

Chapter 2 describes statistical methods applied in pandas objects and manipulation with data inside pandas object. It describes pandas operations such as: unique values, value counting, manipulation with missing data, filtering and filling missing data.

Chapter 3 talks about reading and writing data from text file format and Microsoft Excel. Partial reading of large text files is also described with an example.

Chapter 4 describes data visualization using matplotlib library. It has example about the following graphs: line, scatter, bar and pie. Setting title, legend and labels in the graph is also describes with some practical examples. Drawing from pandas object is also described.

Chapter 5 talks about data wrangling. Merging Series object and DataFrame object is described with practical examples. Combining pandas objects and merging them is part of this chapter.

Chapter 6 talks about various forms of data aggregation and grouping. Creating and using pivot tables is also described.

Chapter 7 talks about time Series creation and manipulation. Classes DatetimeIndex and Period are included in the description of the chapter. Indexing and selection is described with practical examples.

Who this course is for:

Aspiring data analyst
Data analyst
Students that want to have knowledge about pandas library

What you'll learn

Explore related topics

Course content

Introduction to pandas library16 lectures • 1hr 55min

Operations in pandas library12 lectures • 1hr 24min

Reading and writing data to the file8 lectures • 56min

Data visualization12 lectures • 1hr 19min

Data wrangling in pandas13 lectures • 1hr 27min

Data grouping and aggregation13 lectures • 1hr 20min

Time series18 lectures • 2hr 18min

Requirements

Description

Who this course is for: