Story: This series of articles assume you are an employee of the City of Sacramento’s IT Department. At the end of each month, a CSV file is sent to the Chief of Police outlining the crime statistics for the current month. However, the Chief prefers to view the output in a styled PDF format based … Read more
Story: This series of articles assume you are an employee of the City of Sacramento’s IT Department. At the end of each month, a CSV file is sent to the Chief of Police outlining the crime statistics for the current month. However, the Chief prefers to view the output in a styled PDF format based … 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
When we’re working on real-world data, the data files that we receive will be huge. In such cases, cleansing the data becomes important. Often, we might have to count the occurrences of elements in a column. Doing so, gives us insights like: The number of times the value has been duplicated. Count corresponding to a … Read more
This article focuses on the serialization and conversion methods of a Python DataFrame: to_parquet(), to_feather(), to_string(), Styler. Let’s get started! 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 … Read more
Problem Formulation While working on large datasets, we might have to find the unique values from various columns. For example, let’s say, we have a dataframe object as shown below: And we want to find the unique elements from Col1 and Col2. That is, we want to return the following values: Solution Overview To solve … Read more
This article focuses on the serialization and conversion methods of a Python DataFrame: to_markdown(), to_stata(), to_hdf(), to_latex(), to_xml(). 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 Tabulate library enables formatted output. The Tables library allows formatted output … Read more
This article focuses on the serialization and conversion methods of a Python DataFrame: to_clipboard(), to_html(), to_sql(), to_csv(), and to_excel() Let’s get started! Preparation Before any data manipulation can occur, two (2) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The Openpyxl library enables conversion to/from Excel. To install these libraries, … Read more
This article focuses on the serialization and conversion methods of a Python DataFrame: from_dict(), to_dict(), from_records(), to_records(), to_json(), and to_pickles(). Let’s get started! Preparation Before any data manipulation can occur, two (2) 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 … Read more
The Pandas DataFrame/Series has several methods related to plotting. 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 Matplotlib library displays a visual graph of … Read more
The Pandas DataFrame/Series has several methods related to plotting. Preparation Before any data manipulation can occur, three (3) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The Matplotlib library displays a visual graph of a plotted dataset. The Scipy library allows users to manipulate and visualize the data. To install … Read more
The Pandas DataFrame/Series has several methods related to time series. Preparation Before any data manipulation can occur, one (1) new library will require installation. The Pandas library enables access to/from a DataFrame. To install this library, navigate to an IDE terminal. At the command prompt ($), execute the code below. For the terminal used in … Read more