DataFrame

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StevenSwiniarski
Published May 12, 2022Updated May 27, 2022
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A DataFrame is the primary object used by the Pandas module to store and manipulate data. It is a structured collection of data arranged in rows and columns, similar to a database table.

Many Pandas functions, such as .read_csv(), return DataFrame objects. Other functions take DataFrame objects and accept them as parameters. In addition, most of Pandas’ functionality is implemented through the DataFrame object. Methods and properties of the DataFrame object are listed below.

DataFrame

.apply()
Used to apply a function along one axis of the DataFrame.
.at[]
Returns a specific value in a DataFrame using the row and column labels.
.columns
Represents the column labels of the DataFrame.
.copy()
Returns a copy of a DataFrame or Series.
.drop()
Returns a DataFrame object with rows or columns removed based on column or index names.
.dropna()
Returns a DataFrame object with rows or columns with NA values removed.
.drop_duplicates()
Returns a DataFrame object with duplicate rows removed.
.fillna()
Returns a DataFrame object with NA values replaced with the specified value.
.groupby()
Groups a DataFrame using a mapper or a series of columns and returns a GroupBy object.
.index
Represents the row labels of the DataFrame.
.insert()
Inserts a new column into the DataFrame at the specified location.
.isna()
Checks whether the objects of a Dataframe or a Series contain missing or null values and returns a corresponding boolean value.
.loc
Accesses specified rows and/or columns of a DataFrame object.
.pop()
Removes a specified column from a DataFrame.
.replace()
Returns a DataFrame object after values within the DataFrame have been changed.
.reset_index()
Resets the index of a DataFrame to be continuous
.shape
Returns the number of rows and columns of given DataFrame in tuple form.
.sort_values()
Sorts values in a DataFrame by one or more selected columns or rows.
.tail()
Returns the last n rows of a DataFrame.

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