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pandas apply return multiple rows
The apply
method in Pandas can be used to apply a function to each row or column of a DataFrame. By default, apply
will return a single value for each row or column, depending on the function being applied.
However, it is possible to return multiple values for each row by using a function that returns a Series or DataFrame. For example, consider the following DataFrame:
pythonimport pandas as pd df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})
We can use apply
to create a new DataFrame that contains multiple rows for each original row of df
. Here’s an example:
pythondef square_and_cube(row): return pd.Series([row['A'] ** 2, row['A'] ** 3]) result = df.apply(square_and_cube, axis=1)
In this case, square_and_cube
is a function that takes a single row of df
, squares the value in column ‘A’, and cubes the value in column ‘A’. The function then returns a Series with the two resulting values.
When we call df.apply(square_and_cube, axis=1)
, Pandas applies square_and_cube
to each row of df
and concatenates the resulting Series objects into a new DataFrame. The resulting DataFrame result
will have two columns and three rows (since df
had three rows).
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How to Use apply() function to Return Multiple Columns in Pandas DataFrame
How to return multiple values in pandas apply?
To return multiple values in a Pandas apply
method, you can return a pandas Series or a tuple of multiple values from the applied function. Here are a few examples:
Example 1: Returning a pandas Series
pythonimport pandas as pd # Create a sample dataframe df = pd.DataFrame({'col1': [1, 2, 3, 4], 'col2': [5, 6, 7, 8]}) # Define a function that returns multiple values as a Series def multiple_values(x): return pd.Series([x['col1']*2, x['col2']*3]) # Apply the function to the dataframe df[['col1_new', 'col2_new']] = df.apply(multiple_values, axis=1) # Display the updated dataframe print(df)
Output:
col1 col2 col1_new col2_new 0 1 5 2 15 1 2 6 4 18 2 3 7 6 21 3 4 8 8 24
Example 2: Returning a tuple of values
pythonimport pandas as pd # Create a sample dataframe df = pd.DataFrame({'col1': [1, 2, 3, 4], 'col2': [5, 6, 7, 8]}) # Define a function that returns multiple values as a tuple def multiple_values(x): return (x['col1']*2, x['col2']*3) # Apply the function to the dataframe df[['col1_new', 'col2_new']] = df.apply(multiple_values, axis=1, result_type='expand') # Display the updated dataframe print(df)
Output:
col1 col2 col1_new col2_new 0 1 5 2 15 1 2 6 4 18 2 3 7 6 21 3 4 8 8 24
In both examples, the apply
method is used to apply the multiple_values
function to each row of the dataframe. The axis=1
argument specifies that the function should be applied to each row, and the result_type
argument specifies how the returned values should be combined into a new dataframe. In the first example, the returned values are combined into a pandas Series with two columns, which are added to the original dataframe. In the second example, the returned values are combined into a tuple, which is then unpacked into two new columns in the original dataframe.
Which method is used to return one or more rows in pandas?
The method used to return one or more rows in pandas is the .loc[]
method.
The .loc[]
method is used to select rows based on a label or a boolean array. To select a single row, you can pass a single label to the .loc[]
method. For example, to select the row with index label 0, you can use the following code:
pythonimport pandas as pd # Create a DataFrame df = pd.DataFrame({'Name': ['Alice', 'Bob', 'Charlie'], 'Age': [25, 30, 35]}) # Select a single row using the .loc[] method row_0 = df.loc[0] print(row_0)
Output:
vbnetName Alice Age 25 Name: 0, dtype: object
To select multiple rows, you can pass a list of labels to the .loc[]
method. For example, to select the first two rows of the DataFrame, you can use the following code:
python# Select multiple rows using the .loc[] method rows_0_1 = df.loc[[0, 1]] print(rows_0_1)
Output:
markdownName Age 0 Alice 25 1 Bob 30
Note that the .loc[]
method can also be used to select rows based on a boolean array.
Which method of pandas can be used for returning last 5 rows?
You can use the tail()
method of Pandas to return the last 5 rows of a dataframe. The tail()
method returns the specified number of rows from the end of the dataframe. By default, it returns the last 5 rows of the dataframe. Here’s an example:
pythonimport pandas as pd # Create a sample dataframe df = pd.DataFrame({'A': [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], 'B': ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']}) # Return the last 5 rows of the dataframe using the tail() method last_5_rows = df.tail() print(last_5_rows)
This will output:
cssA B 5 6 f 6 7 g 7 8 h 8 9 i 9 10 j
In this example, the tail()
method returns the last 5 rows of the df
dataframe.
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pandas apply return dataframe
Yes, pandas apply()
method can return a dataframe, a series, or a scalar depending on the function applied and the axis along which the operation is performed.
If you apply a function to a pandas dataframe using the apply()
method with the axis=0
or axis=1
parameter, the resulting object can be a dataframe or a series.
If the function returns a series, the result will be a dataframe where each element in the returned series will form a new column in the resulting dataframe, with the same index as the original dataframe.
Here’s an example that demonstrates how the apply()
method can return a dataframe:
pythonimport pandas as pd # Create a pandas dataframe df = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}) # Define a function that returns a series def multiply_cols(row): return pd.Series({'C': row['A']*row['B'], 'D': row['A']*row['B']*2}) # Apply the function to the dataframe along axis 1 result = df.apply(multiply_cols, axis=1) print(result)
Output:
C D 0 4 8 1 10 20 2 18 36
In this example, the multiply_cols()
function takes a row of the input dataframe and returns a new series with two elements: the product of columns A and B, and twice that product. When we apply this function to the dataframe along axis=1
, we get a new dataframe with the resulting series as columns ‘C’ and ‘D’.
dask apply return multiple columns
Yes, it’s possible for a Dask apply
operation to return multiple columns.
When you apply a function to a Dask DataFrame or Series, you can return a Pandas DataFrame or Series object from that function. If the returned object has multiple columns, then Dask will automatically create a Dask DataFrame with those columns.
Here’s an example using Dask DataFrame:
pythonimport dask.dataframe as dd import pandas as pd # create a Dask DataFrame df = dd.from_pandas(pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]}), npartitions=2) # define a function that returns multiple columns def my_func(x): return pd.DataFrame({'C': x['A'] + x['B'], 'D': x['A'] * x['B']}) # apply the function to the DataFrame result = df.apply(my_func, axis=1, meta={'C': 'f8', 'D': 'f8'}) # print the result print(result.compute())
In this example, the my_func
function takes a Pandas Series object (x
) and returns a DataFrame with two columns (C
and D
). We then apply this function to our Dask DataFrame (df
) using the apply
method, specifying the axis=1
argument to apply the function to each row. We also specify the meta
argument to tell Dask the expected data types of the output columns.
The resulting Dask DataFrame (result
) will have two columns (C
and D
), and we can call compute()
to trigger the computation and get a Pandas DataFrame as the output.
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