Describing data with pandas DataFrames

Luckily, pandas has descriptive statistics utilities. We will read the average wind speed, temperature, and pressure values from the KNMI De Bilt data file into a pandas DataFrame. This object is similar to the R dataframe, which is like a data table in a spreadsheet or a database. The columns are labeled, the data can be indexed, and you can run computations on the data. We will then print out descriptive statistics and a correlation matrix as shown in the following steps:

  1. Read the CSV file with the pandas read_csv function. This function works in a similar fashion to the NumPy load_txt function:
    to_float = lambda x: .1 * float(x.strip() or np.nan) to_date = lambda x: dt.strptime(x, "%Y%m%d") cols = [4, 11, ...

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