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Pandas DataFrame ‘groupby()’ Method.
Pandas DataFrame groupby() Method. Table Of Contents: Syntax ‘groupby( )’ Method In Pandas. Examples ‘groupby( )’ Method. (1) Syntax: DataFrame.groupby(by=None, axis=0, level=None, as_index=True, sort=True, group_keys=_NoDefault.no_default, squeeze=_NoDefault.no_default, observed=False, dropna=True) Description: Group related records together and apply some aggregate function. A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups. Parameters: by: mapping, function, label, or list of labels- Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index.
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Pandas DataFrame ‘applymap()’ Method.
Pandas DataFrame ‘applymap()’ Method. Table Of Contents: Syntax ‘applymap( )’ Method In Pandas. Examples ‘applymap( )’ Method. Difference Between apply() and applymap() . (1) Syntax: DataFrame.applymap(func, na_action=None, **kwargs) Description: Apply a function to a Dataframe elementwise. This method applies a function that accepts and returns a scalar to every element of a DataFrame. Parameters: func: callable – Python function, returns a single value from a single value. na_action: {None, ‘ignore’}, default None – If ‘ignore’, propagate NaN values, without passing them to func. **kwargs – Additional keyword arguments to pass as keywords arguments to func. Returns: DataFrame : Transformed DataFrame. (2) Examples
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How To Apply Function To A DataFrame?
How To Apply Function To A DataFrame? Table Of Contents: Syntax ‘apply( )’ Method In Pandas. Examples ‘apply( )’ Method. (1) Syntax: DataFrame.apply(func, axis=0, raw=False, result_type=None, args=(), **kwargs) Description: Apply a function along an axis of the DataFrame. Parameters: func: function- Function to apply to each column or row. axis{0 or ‘index’, 1 or ‘columns’}, default 0 – Axis along which the function is applied: 0 or ‘index’: apply the function to each column. 1 or ‘columns’: apply the function to each row. rawbool, default False – Determines if row or column is passed as a Series or ndarray object:
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How To Apply Query To A DataFrame?
How To Apply Query To A DataFrame? Table Of Contents: Syntax ‘query( )’ Method In Pandas. Examples ‘query( )’ Method. (1) Syntax: DataFrame.query(expr, *, inplace=False, **kwargs) Description: Query the columns of a DataFrame with a boolean expression. Parameters: expr: str – The query string to evaluate. You can refer to variables in the environment by prefixing them with an ‘@’ character like @a + b. You can refer to column names that are not valid Python variable names by surrounding them in backticks. Thus, column names containing spaces or punctuations (besides underscores) or starting with digits must be surrounded by backticks. (For example,
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Pandas DataFrame ‘mask()’ Method.
Pandas DataFrame ‘mask( )’ Method. Table Of Contents: Syntax Of ‘mask( )’ Method In Pandas. Examples ‘mask( )’ Method. (1) Syntax: DataFrame.mask(cond, other=nan, *, inplace=False, axis=None, level=None, errors=’raise’, try_cast=_NoDefault.no_default) Description: Replace values where the condition is True. Where cond is False, keep the original value. Where True, replace with the corresponding value from other. Parameters: cond: bool Series/DataFrame, array-like, or callable – Where cond is False, keep the original value. Where True, replace with the corresponding value from other. other: scalar, Series/DataFrame, or callable – Entries where cond is True are replaced with the corresponding value from other. in place: bool, default False – Whether to
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Pandas DataFrame ‘where( )’ Method.
Pandas DataFrame ‘where( )’ Method. Table Of Contents: Syntax ‘where( )’ Method In Pandas. Examples ‘where( )’ Method. (1) Syntax: DataFrame.where(cond, other=_NoDefault.no_default, *, inplace=False, axis=None, level=None, errors='raise', try_cast=_NoDefault.no_default) Description: Return you the records where the condition satisfy. Replace values with ‘NaN’ where the condition is Not Satisfying. Where cond is True, keep the original value. Where False, replace with the corresponding value from other. Parameters: cond: bool Series/DataFrame, array-like, or callable – Where cond is True, keep the original value. Where False, replace with the corresponding value from other. other: scalar, Series/DataFrame, or callable – Entries where cond is False are replaced with the corresponding
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Pandas DataFrame isin() Method.
Pandas DataFrame isin() Method. Table Of Contents: Syntax isin( ) Method. Examples isin( ) Method. (1) Syntax: DataFrame.isin(values) Description: The isin() method checks if the Dataframe contains the specified value(s). It returns a DataFrame similar to the original DataFrame, but the original values have been replaced with True if the value was one of the specified values, otherwise False. Parameters: values: iterable, Series, DataFrame or dict – – The result will only be true at a location if all the labels match. If values is a Series, that’s the index. If values is a dict, the keys must be the column names, which must match. If values is a DataFrame, then
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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
