Problem Formulation 💬 Given a text query/keyword such as “History of Chess”. How to scrape the top Google results for that search query (=keyword) in Python? Disclaimer: Have a look at the important question: Is Web Scraping Legal? Method Summary You can get the top Google search results given a certain keyword string by installing … Read more
What is Data Cleaning? Data cleaning describes the process of turning messy data into clean datasets that can be used for research and data science purposes. For example, tidy data will be in a wide format: every column contains a variable, and every row contains one case. Also, data cleaning means getting rid of corrupt … Read more
Problem Formulation and Solution Overview This article will show you how to count duplicates in a Pandas DataFrame in Python. To make it more fun, we have the following running scenario: Rivers Clothing has a CSV containing all its employees. However, their CSV file has more rows than employees. This is a definite problem! They … Read more
Problem Formulation and Solution Overview In this article, you’ll learn how to add a new column to a CSV file in Python. To make it more fun, we have the following running scenario: The owner of the Finxter Academy has asked you to add a new column to their existing CSV file called Total_Chrgs. 💬 … Read more
Summary: Use the string literal syntax f”{number:.nf}” to suppress the scientific notation of a number to its floating-point representation. Problem Formulation: How will you suppress a number represented in scientific notation by default to a floating-point value? Note: Generally, Python represents huge floating-point numbers or very small floating-point numbers in their scientific form. Scientific notation … Read more
Problem: Given a CSV file, how to read only specific column(s) from the csv file? (Reading a specific column from a csv file will yield all the row values pertaining to that column.) Example: Consier the following csv file (countries.csv): Question: How will you read the above csv file and display the following columns – … Read more
Preparation Before any data manipulation can occur, four (4) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical functions. The pandas_gbq allows access to Google Big Query (GBQ) The google.auth authentication. To install these libraries, navigate … Read more
This article focuses on the serialization and conversion methods of a Python DataFrame: to_gbq(), to_coo(). Let’s get started! Preparation Before any data manipulation can occur, four (4) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The NumPy library supports multi-dimensional arrays and matrices in addition to a collection of mathematical … Read more
Preparation Before any data manipulation can occur, three (3) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The Pyarrow library allows writing/reading access to/from a parquet file. The Openpyxl library allows styling/writing/reading to/from an Excel file. To install these libraries, navigate to an IDE terminal. At the command prompt ($), … Read more
Preparation Before any data manipulation can occur, three (3) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The Pyarrow library allows writing/reading access to/from a parquet file. The Openpyxl library allows styling/writing/reading to/from an Excel file. To install these libraries, navigate to an IDE terminal. At the command prompt ($), … Read more
Preparation Before any data manipulation can occur, three (3) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The Pyarrow library allows writing/reading access to/from a parquet file. The Openpyxl library allows styling/writing/reading to/from an Excel file. To install these libraries, navigate to an IDE terminal. At the command prompt ($), … Read more
Preparation Before any data manipulation can occur, four (4) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The Tabulate library enables formatted output. The Tables library allows formatted output (table format). The lxml library enables writing to an XML file. To install these libraries, navigate to an IDE terminal. At … Read more