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How To Remove Columns Of A DataFrame?
How To Remove Columns Of A DataFrame? Table Of Contents: Syntax For Removing Columns Of Data Frame. Examples Of Column Removal. (1) Syntax: DataFrame.pop(item) Description: Return the item and drop it from the frame. Raise KeyError if not found. Parameters: item: label – Label of column to be popped. Returns: Series – The Dropped Column (2) Examples Of pop() Method: Example-1 import pandas as pd student = {‘Name’:[‘Subrat’,’Abhispa’,’Arpita’,’Anuradha’,’Namita’], ‘Roll_No’:[100,101,102,103,104], ‘Subject’:[‘Math’,’English’,’Science’,’History’,’Commerce’], ‘Mark’:[95,88,76,73,93]} student_object = pd.DataFrame(student) student_object Output: dropped = student_object.pop(‘Mark’) dropped Output: 0 95 1 88 2 76 3 73 4 93 Name: Mark, dtype: int64 Note: The ‘pop()’ method has
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Pandas itertuples() Method.
Pandas itertuples() Method. Table Of Contents: Syntax For itertuples() Method Examples Of itertuples() Method. (1) Syntax: DataFrame.itertuples(index=True, name=’Pandas’) Description: Iterate over DataFrame rows as named tuples. Parameters: index: bool, default True – If True, return the index as the first element of the tuple. name: str or None, default “Pandas” – The name of the returned namedtuples or None to return regular tuples. Returns: iterator An object to iterate over named tuples for each row in the DataFrame with the first field possibly being the index and the following fields being the column values. (2) Examples Of itertuples() Method: Example-1
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How To Iterate Over Rows Of A DataFrame?
How To Iterate Over Rows Of A DataFrame? Table Of Contents: Syntax For Iterating Over Rows Of Data Frame. Examples Of Rows Iteration. (1) Syntax: DataFrame.iterrows() Description: Iterate over DataFrame rows as (index, Series) pairs. Returns: index:label or tuple of labelThe index of the row. A tuple for a MultiIndex. data: Series The data of the row as a Series. (2) Examples Of iterrows() Method: Example-1 import pandas as pd student = {‘Name’:[‘Subrat’,’Abhispa’,’Arpita’,’Anuradha’,’Namita’], ‘Roll_No’:[100,101,102,103,104], ‘Subject’:[‘Math’,’English’,’Science’,’History’,’Commerce’], ‘Mark’:[95,88,76,73,93]} student_object = pd.DataFrame(student) student_object Output: for label, content in student_object.iterrows(): print(f’Columns: {label}’) Output: Columns: 0 Columns: 1 Columns: 2 Columns: 3 Columns: 4 Note: It
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How To Iterate Over Columns Of A DataFrame?
How To Iterate Over Columns Of A DataFrame? Table Of Contents: Syntax For Iterating Over Columns Of Data Frame. Examples Of Column Iteration. (1) Syntax: DataFrame.items() Description: Iterate over (column name, Series) pairs. Iterates over the DataFrame columns, returning a tuple with the column name and the content as a Series. Returns: label: object- The column names for the DataFrame being iterated over. content: Series – The column entries belonging to each label, as a Series. (2) Examples Of items() Method: Example-1 import pandas as pd student = {‘Name’:[‘Subrat’,’Abhispa’,’Arpita’,’Anuradha’,’Namita’], ‘Roll_No’:[100,101,102,103,104], ‘Subject’:[‘Math’,’English’,’Science’,’History’,’Commerce’], ‘Mark’:[95,88,76,73,93]} student_object = pd.DataFrame(student) student_object Output: for label, content
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How To Insert A Column To A DataFrame?
How To Insert A Column To A DataFrame? Table Of Contents: Syntax For inserting A Column. Examples Of Inserting A Column. (1) Syntax: DataFrame.insert(loc, column, value, allow_duplicates=_NoDefault.no_default) Description: Insert column into DataFrame at the specified location. Raises a ValueError if the column is already contained in the DataFrame, unless allow_duplicates is set to True. Parameters: loc: int – Insertion index. Must verify 0 <= loc <= len(columns). column: str, number, or hashable object Label of the inserted column. value: Scalar, Series, or array-like allow_duplicates: bool, optional, default lib.no_default (2) Examples Of ‘iloc’ Keyword: Example-1 import pandas as pd student = {‘Name’:[‘Subrat’,’Abhispa’,’Arpita’,’Anuradha’,’Namita’], ‘Roll_No’:[100,101,102,103,104],
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Pandas DataFrame ‘iloc’ Keyword.
Pandas DataFrame ‘iloc’ Keyword. Table Of Contents: Syntax For ‘iloc’ Keyword In Data Frame. Examples Of ‘iloc’ Keyword. (1) Syntax: pandas.DataFrame.iloc Description: Purely integer-location-based indexing for selection by position. .iloc[] is primarily integer position based (from 0 to length-1 of the axis), but may also be used with a boolean array. Allowed inputs are: An integer, e.g. 5. A list or array of integers, e.g. [4, 3, 0]. A slice object with ints, e.g. 1:7. A boolean array. A callable function with one argument (the calling Series or DataFrame) and that returns valid output for indexing (one of the above). This is useful in method chains, when you don’t have a reference
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Pandas DataFrame ‘loc’ Keyword
Pandas DataFrame ‘loc’ Keyword. Table Of Contents: Syntax For ‘loc’ Keyword In Data Frame. Examples Of ‘loc’ Keyword. (1) Syntax: pandas.DataFrame.loc Description: Access a group of rows and columns by label(s) or a boolean array. .loc[] is primarily label based, but may also be used with a boolean array. Allowed inputs are: A single label, e.g. 5 or ‘a’, (note that 5 is interpreted as a label of the index, and never as an integer position along the index). A list or array of labels, e.g. [‘a’, ‘b’, ‘c’]. A slice object with labels, e.g. ‘a’:’f’. A boolean array of the same length as the axis being sliced, e.g. [True, False, True]. An alignable boolean Series. The
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Pandas DataFrame ‘iat’ Keyword.
Pandas DataFrame ‘iat’ Keyword. Table Of Contents: Syntax Of ‘iat’ Keyword Of Data Frame. Examples Of ‘iat’ Keyword. (1) Syntax: pandas.DataFrame.iat Description: Access a single value for a row/column pair by integer position. Here ‘i’ signifies the index of the column. The index always starts with a Zero position. Raises: IndexError : When integer position is out of bounds. (2) Examples Of ‘iat’ Keyword: Example-1 import pandas as pd student = {‘Name’:[‘Subrat’,’Abhispa’,’Arpita’,’Anuradha’,’Namita’], ‘Roll_No’:[100,101,102,103,104], ‘Subject’:[‘Math’,’English’,’Science’,’History’,’Commerce’], ‘Mark’:[95,88,76,73,93]} student_object = pd.DataFrame(student) student_object Output: Getting Record At Row ‘1’ And Column ‘Name’ student_object.iat[2,0] Note: ‘2’ – Represents The Row Index. ‘0’ – Represents The
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Pandas DataFrame ‘at’ Keyword.
Pandas DataFrame ‘at’ Keyword. Table Of Contents: Syntax For ‘at’ Keyword In Data Frame. Examples Of ‘at’ Keyword. (1) Syntax: pandas.DataFrame.at Description: Access a single value for a row/column label pair. Raises KeyError: If getting a value and ‘label’ does not exist in a DataFrame or Series. ValueError: If row/column label pair is not a tuple or if any label from the pair is not a scalar for DataFrame. If label is list-like (excluding NamedTuple) for Series. (2) Examples Of ‘at’ Keyword: Example-1 import pandas as pd student = {‘Name’:[‘Subrat’,’Abhispa’,’Arpita’,’Anuradha’,’Namita’], ‘Roll_No’:[100,101,102,103,104], ‘Subject’:[‘Math’,’English’,’Science’,’History’,’Commerce’], ‘Mark’:[95,88,76,73,93]} student_object = pd.DataFrame(student) student_object Output: Getting Record At
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How To Display Bottom ‘n’ Records Of DataFrame?
How To Display Bottom ‘n’ Records Of DataFrame? Table Of Contents: Syntax To Display Bottom Records Of Data Frame. Examples Of Displaying Bottom Records. (1) Syntax: DataFrame.tail(n=5) Description: Return the last n rows. This function returns the last n rows for the object based on position. It is useful for quickly testing if your object has the right type of data in it. For negative values of n, this function returns all rows except the last |n| rows, equivalent to df[:n]. If n is larger than the number of rows, this function returns all rows. Parameters: n: int, default 5 : Number of rows to select.
