Previously, we used Pandas to read and write data to CSV files, reshape data from wide to long format, and used the Seaborn package to plot paired data. Here, we will summarise some data and write the results to a CSV file. The CSV file data_pandas.csv (available here) contains simulated data of age, height and score from 20 subjects. To
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Pandas (i.e. panel data) is a Python library designed to manipulate data in tables and time series. Pandas uses many Numpy library functions to manipulate data stored in dataframes, analogous to a spreadsheet or table. Let’s look at some basic Pandas functions to manipulate data, and plot the data using the Seaborn plotting package. To begin, import libraries and simulate
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Comma separated values (CSV) files are a type of text file commonly used to store data. In a CSV file, each line of text contains values separated with commas. CSV files can be imported into Python in different ways (eg. csv.reader, numpy.loadtxt, etc). One useful method is to import CSV files into Pandas dataframes. Pandas package. Pandas is a Python
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