Kat McKelvie

At university, I found my love of writing and coding. Both of which I was able to use in my career.During the past 15 years, I have held a number of positions such as:In-house Corporate Technical Writer for various software programs such as Navision and Microsoft CRM Corporate Trainer (staff of 30+) Programming Instructor Implementation Specialist for Navision and Microsoft CRM Senior PHP Coder

Serialize DataFrame to Parquet, Feather, String, Styler

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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

Serialize DataFrame to Markdown, Strata, HDF, LaTex, XML

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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

Serialize DataFrame to Clipboard, HTML, SQL, CSV, Excel

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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

Serialize DataFrame – Dict, Records, JSON, and Pickles

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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

Pandas Hexbin, Hist, Pie, Scatter Plot

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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

Pandas Plotting – plot() & plot.area()

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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

Pandas resample(), to_period(), to_timestamp(), tz_localize(), tz_convert()

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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

Pandas asfreq(), asof(), shift(), slice_shift(), tshift(), first_valid_index(), last_valid_index()

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The Pandas DataFrame/Series has several methods related to time series. 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 to a collection of mathematical functions. To install these libraries, navigate to an … Read more

Pandas append(), assign(), compare(), join(), merge(), update()

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The Pandas DataFrame/Series has several methods to combine/compare/join and merge the data. 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 to a collection of mathematical functions. To install these libraries, navigate … Read more

Pandas melt(), explode(), squeeze(), to_xarray(), transpose()

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The Pandas DataFrame/Series has several methods to re-shape, sort, and transpose the data. Preparation Before any data manipulation can occur, two (2) new libraries will require installation. The Pandas library enables access to/from a DataFrame. The Xarray library works with labeled multi-dimensional arrays and advanced analytics. To install these libraries, navigate to an IDE terminal. … Read more

Pandas nlargest(), nsmallest(), swap_level(), stack(), unstack(), swap_axes()

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The Pandas DataFrame/Series has several methods to re-shape, sort, and transpose the data. When applied to a DataFrame/Series, these methods evaluate and modify the data to accommodate the selections. 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 … Read more

Pandas drop_level(), pivot(), pivot_table(), reorder_levels(), sort_values(), sort_index()

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The Pandas DataFrame/Series has several methods to handle Missing Data. When applied to a DataFrame/Series, these methods evaluate and modify the missing elements. 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