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Solve DtypeWarning: Columns have mixed types. Specify dtype option on import or set low_memory=False in Pandas

The title of this blog post says it all: let’s solve a warning. It’s one that occurs when you’re using Pandas’ read_csv. Let’s start by just doing that. Let’s say you run into the following error: Solve DtypeWarning: Columns (X,X) have mixed types. Specify dtype option on import or set… 

Pandas: Solve “You are trying to merge on datetime64[ns] and Object columnS”

Without a doubt, Pandas is one of the most popular Python packages within Data Science. However, coming from an R background, discovering the difference between the join and merge method was mind-boggling. Especially if it generates confusing errors that urge you to use pd.concat, which you shouldn’t. This is the… 

Solved: “The caller does not have permission” – Using the API with a private Google Spreadsheet

Ahh, user rights. The cause and solution to all of life’s identification problems. In this blog post I explain how you can access (private) google spreadsheets using the Python gspread library. Before you get started: make sure you have administrator rights to the spreadsheets you are trying to work with.… 

Pandas’ pivot_table vs. pivot

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  • 4 min read

When you’re an R poweruser, pivoting tables in pandas feels unnecessarily complex. Why are there two pivot functions? Why does it return an index when you wanted a column? Why does it generate multi index columns? Those are the questions I tackle in this blog post. 💥 This blog post… 

Working with NaN’s (nulls/NA’s) in pandas: per column, per row and per group

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  • 2 min read

Getting a firm understanding of NaNs in your dataset ensures you don’t draw wrong conclusions from an incomplete dataset. In this blog post I show how you can list the amount of NaNs per column, per row, and per group. First, let’s create some dummy data, and add some NaNs.…