Download Data Analysis & Plotting for Chemistry with Python 2025
Data Analysis & Plotting for Chemistry with Python video course download, Master data handling, visualization, and real-world chemical analysis with Python—no coding or data science background. Chemistry generates data at every level — from titration curves and kinetic measurements to spectroscopy outputs and thermodynamic models. Yet, many chemists struggle to efficiently analyze and visualize this data in a way that leads to clear insights and impactful communication.
Starting with the basics of Python, you will quickly progress to mastering essential libraries such as NumPy and Pandas, for handling and analyzing chemical datasets. You will then learn to create high-quality plots using Matplotlib ensuring your graphs are not just scientifically accurate but also publication ready. Every concept is explained in the context of chemistry, with real datasets and case studies drawn from spectroscopy, kinetics, thermodynamics, and analytical chemistry.
By the end of the course, you will be able to clean and organize experimental data, perform numerical analyses, and present your results with professional-grade visualizations. Whether you are a student preparing lab reports, a researcher analyzing experiments, or a professional aiming to improve reporting workflows, this course will give you the practical skills to transform raw chemical data into clear, meaningful insights. Moreover, this course will pave your easy way to machine learning where large datasets are handled, analyzed and interpreted.
What you’ll learn
- Learn to efficiently manage, clean, and preprocess chemical datasets using Python.
- Import, clean, and manipulate raw chemical data using Python libraries (pandas, numpy, etc.).
- Generate professional-quality plots (2D, 3D, interactive) tailored to chemistry problems.
- Analyze spectroscopic, thermodynamic, and kinetic datasets to draw research-level conclusions.
- Reproduce and interpret data analyses from published chemistry research articles
- Apply best practices in reproducibility, documentation, and data-driven research.
Course content
Course content
- Section 1: Introduction
- Lecture 1 Introduction
- Section 2: Numpy
- Lecture 2 setting up a colab environment
- Lecture 3 Problems/Limitations of Python Lists
- Lecture 4 Tan of 10 values between 0 and 2Pi using list and for loop
- Lecture 5 Tan of 10 values between 0 and 2Pi using NumPy
- Lecture 6 Generate array of equally spaced values through Lin space
- Lecture 7 Square of an array
- Lecture 8 Some other functions on an array in NumPy
- Lecture 9 Problem: Rate constant of a first order reaction through Arrhenius equation
- Lecture 10 Equally space array using step size (arrange)
- Lecture 11 Problem: To solve for particle in one dimensional box
- Lecture 12 Two dimensional arrays (arrays of zeros and ones)
- Lecture 13 Problem: Electronic configuration of Iron in the form of table using Numpy Zero
- Lecture 14 Reshaping of a two-dimensional array
- Lecture 15 Complex Reshaping of an array
- Lecture 16 NumPy array from Function
- Lecture 17 Indexing of an array
- Lecture 18 changing an item in an array
- Lecture 19 some other functions in indexing and slicing of an array
- Lecture 20 Reversing an array
- Lecture 21 Mean, standard deviation of array
- Lecture 22 max min argmax argmin of an array
- Lecture 23 column wise indexing, argmax, …..
- Lecture 24 Compound Indexing of an array
- Lecture 25 Masking of an array
- Lecture 26 Applying condition on an array
- Lecture 27 Math vs numpy for an operation on an array
- Lecture 28 multiplication and divisions of an array with another one
- Lecture 29 Math operation (addition, multiplication division) on arrays of diff dimension
- Lecture 30 Math operation (addition, multiplication division) on arrays of same dimension
- Lecture 31 NumPy Vectorize operation for a function
- Lecture 32 Opening a file with NumPy
- Lecture 33 Save Ionization potential data into a csv file using NumPy
- Lecture 34 arguments in opening a file suing NumPy and dealing with empty numbers
- Lecture 35 Genomyx and loadtxt options to open a file
- Section 3: Pandas
- Lecture 36 Limitation of NumPy in handling strings in data
- Lecture 37 Panda Series of melting points of the first five elements and the indexing
- Lecture 38 Panda Series from a dictionary of melting points and indexing by elements
- Lecture 39 Pandas data frame of AN, AM, d, mp and bp of elements
- Lecture 40 Table of properties of elements using Pandas Data frame index and column
- Lecture 41 Modifying data frame and selectively printing a property from a data frame
- Lecture 42 Indexing of a data frame (loc)
- Lecture 43 Indexing of a series of heat of combustion of alkenes
- Lecture 44 heat of enthalpies in kJ per gram by dividing series with molar mass
- Lecture 45 Performing a boolean test on a series and to find an item in the series
- Lecture 46 Sorting of a series by index and value
- Lecture 47 Exporting the elemental properties to a csv file and read from it.
- Lecture 48 Reading a pdb file as a table using Pandas and the arguments in opening a file
- Lecture 49 Exporting the pdb data to csv and excel using Pandas
- Lecture 50 Adding the properties of element to a data frame by appending or by adding series
- Lecture 51 Droping an entrie row or column from a data frame
- Lecture 52 Merging two data frames
- Lecture 53 Generating a table of proton and neutrons of transition metals
- Lecture 54 Enthalpy and entropy of fusion of transition metals using Pandas
- Section 4: Plotting of a spectrum through MatPlotLib.PyPlot
- Lecture 55 Plot to show Solubility of a compound in methanol as a function of temperature
- Lecture 56 Modifying a plot by changing color, marker, line width etc
- Lecture 57 A combined plot of solubilities of comp A and B and export the plot in PNG format
- Lecture 58 bar plot of ionization energies and electron affinities of first 10 elements
- Lecture 59 scatter plot of alcohol contents against alkalinity of ash and proline content
- Lecture 60 Histogram of ionization potentials of elements
- Lecture 61 STEM Plot of cosine of radians from 0 to 15
- Lecture 62 Pie chart of natural abundances of 8 most abundant elements in earth crust
- Lecture 63 step plot of natural abundances of 8 most abundant elements in earth crust
- Lecture 64 Plotting more than one subplot in a figure
- Lecture 65 Plotting more than one subplot in a figure- A second approach
Course details
- Video quality: MP4 | Video: h264, 1280 × 720
- Audio quality: Audio: AAC, 44.1 KHz, 2 Ch
- Last updated 8/2025
- Video duration: 3h 59m
- Number of lessons: 5 sections, 65 lectures
- Language: Language: English
- Compressed file size: 1.8 GB
