Data structures pandas

  • How Pandas determine data types?

    To check the data type in pandas DataFrame we can use the "dtype" attribute.
    The attribute returns a series with the data type of each column.
    And the column names of the DataFrame are represented as the index of the resultant series object and the corresponding data types are returned as values of the series object..

  • How Pandas reshape data?

    Pandas has two methods that aid in reshaping the data into a desired format.
    Pandas has two methods namely, melt() and pivot(), to reshape the data.
    These methods work similar to gather() and spread() functions of the 'tidyr' package in R, respectively..

  • There are 3 data structures provided by the Pandas module, which are as follows: Series: It is a 1-D size-immutable array like structure having homogeneous data.
    DataFrames: It is a 2-D size-mutable tabular structure with heterogeneously typed columns.
    Panel: It is a 3-D, size-mutable array.
The most widely used pandas data structures are the Series and the DataFrame. Simply, a Series is similar to a single column of data while a DataFrame is similar to a sheet with rows and columns. Likewise, a Panel can have many DataFrames.

What is a Dataframe in pandas?

A DataFrame is a two-dimensional labeled data structure

Think of it as a table or a spreadsheet

DataFrames are more versatile than Series and are extensively used in Pandas

Data can be sourced from various formats including CSV files, SQLite databases, or Excel files

DataFrames allow fine-grained access to rows and columns


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