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Articles on Trending Technologies
Technical articles with clear explanations and examples
Python Pandas - Return the Transpose of the index
To return the Transpose of the index, use the index.T property.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index(['Car', 'Bike', 'Truck', 'Ship', 'Airplane']) Display the index −print("Pandas Index...", index)Display the transpose of the index −print("Transpose of the Pandas Index which is by definition self...", index.T) ExampleFollowing is the code −import pandas as pd # Creating the index index = pd.Index(['Car', 'Bike', 'Truck', 'Ship', 'Airplane']) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Display the transpose of the index ...
Read MorePython Pandas - Return an IntervalArray identical to the current one but closed on the specified side
To return an IntervalArray identical to the current one but closed on the specified side, use the array.set_closed() with parameter both.At first, import the required libraries −import pandas as pdConstruct a new IntervalArray from an array-like of splits −array = pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3, 4, 5])Display the intervals −print("Our IntervalArray...", array)An IntervalArray identical to the current one but closed on the specified side −print("An identical IntervalArray closed on the specified side...", array.set_closed('both'))ExampleFollowing is the code −import pandas as pd # Construct a new IntervalArray from an array-like of splits array = pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3, 4, 5]) # ...
Read MorePython Pandas - Check elementwise if the Intervals contain the value
To check elementwise if the Intervals contain the value, use the array.contains() method.At first, import the required libraries −import pandas as pdConstruct a new IntervalArray from an array-like of splits −array = pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3, 4, 5]) Display the intervals −print("Our IntervalArray...", array)Check whether the Interval contain a specific value −print("Does the Intervals contain the value? ", array.contains(3.5)) ExampleFollowing is the code −import pandas as pd # Construct a new IntervalArray from an array-like of splits array = pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3, 4, 5]) # Display the IntervalArray print("Our IntervalArray...", array) # Getting the length of ...
Read MorePython Pandas - Create an IntervalArray from an array of splits and check the intervals are closed on the left or right-side, both or neither
To create an IntervalArray from an array of splits, use the pandas.arrays.IntervalArray.from_breaks().To check the intervals are closed on the left or right-side, both or neither, use the array.closed property.At first, import the required libraries −import pandas as pdConstruct a new IntervalArray from an array-like of splits. The intervals are closed on the "right" by default −array = pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3, 4, 5]) Display the intervals −print("Our IntervalArray...", array)Check whether the intervals in the Interval Array is closed on the left-side, right-side, both or neither −print("Checking whether the intervals is closed...", array.closed) ExampleFollowing is the code −import pandas as pd ...
Read MorePython Pandas - Construct an IntervalArray from an array of splits
To construct an IntervalArray from an array of splits, use the pandas.arrays.IntervalArray.from_breaks().At first, import the required libraries −import pandas as pdConstruct a new IntervalArray from an array-like of splits −array = pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3, 4, 5]) Display the intervals −print("Our IntervalArray...", array)Getting the length of IntervalArray −print("Our IntervalArray length...", array.length) ExampleFollowing is the code −import pandas as pd # Construct a new IntervalArray from an array-like of splits array = pd.arrays.IntervalArray.from_breaks([0, 1, 2, 3, 4, 5]) # Display the IntervalArray print("Our IntervalArray...", array) # Getting the length of IntervalArray # Returns an Index with entries denoting ...
Read MorePython Pandas - Create a DataFrame from original index but enforce a new index
To create a DataFrame from original index but enforce a new index, use the index.to_frame(). Set the parameter index to False.At first, import the required libraries −import pandas as pdCreating Pandas index −index = pd.Index(['Electronics', 'Accessories', 'Decor', 'Books', 'Toys'], name ='Products')Display the Pandas indexprint("Pandas Index...", index) Enforce new index and convert index to DataFrame. Here, the actual index gets replaced by another index −print("Index to DataFrame...", index.to_frame(index=False))ExampleFollowing is the code −import pandas as pd # Creating Pandas index index = pd.Index(['Electronics', 'Accessories', 'Decor', 'Books', 'Toys'], name ='Products') # Display the Pandas index print("Pandas Index...", index) # Return ...
Read MorePython Pandas - Create a DataFrame with both the original index and name
To create a DataFrame with both the original index and name, use the index.to_frame() method in Pandas.At first, import the required libraries −import pandas as pdCreating Pandas index −index = pd.Index(['Electronics', 'Accessories', 'Decor', 'Books', 'Toys'], name ='Products')Display the Pandas index −print("Pandas Index...", index)Convert index to DataFrame −print("Index to DataFrame...", index.to_frame())ExampleFollowing is the code −import pandas as pd # Creating Pandas index index = pd.Index(['Electronics', 'Accessories', 'Decor', 'Books', 'Toys'], name ='Products') # Display the Pandas index print("Pandas Index...", index) # Return the number of elements in the Index print("Number of elements in the index...", index.size) # Return ...
Read MorePython Pandas - Create an Index with values cast to dtypes
To create an Index with values cast to dtypes, use the index.astype() method in Pandas. At first, import the required libraries −import pandas as pdCreating Pandas index −index = pd.Index([50.4, 10.2, 70.5, 110.5, 90.8, 50.6]) Display the Pandas index −print("Pandas Index...", index)Convert datatype to int64 −index.astype('int64') ExampleFollowing is the code −import pandas as pd # Creating Pandas index index = pd.Index([50.4, 10.2, 70.5, 110.5, 90.8, 50.6]) # Display the Pandas index print("Pandas Index...", index) # Return the number of elements in the Index print("Number of elements in the index...", index.size) # Return the dtype of the ...
Read MorePython - Show which entries in a Pandas Index are not NA
To show which entries in a Pandas Index are not NA, use the index.notna() method. At first, import the required libraries -import pandas as pd import numpy as npCreating Pandas index with some NaN values −index = pd.Index([5, 65, np.nan, 17, 75, np.nan]) Display the Pandas index −print("Pandas Index...", index)Show which entries in a Pandas index are not-NA. Return True for non-NA entries −print("Check which entries are not-NA...", index.notna()) ExampleFollowing is the code −import pandas as pd import numpy as np # Creating Pandas index with some NaN values index = pd.Index([5, 65, np.nan, 17, 75, np.nan]) # ...
Read MorePython Pandas - Drop the value when any level is NaN in a Multi-index
To drop the value when any level is NaN in a Multi-index, use the multiIndex.dropna() method. Set the parameter how with value any.At first, import the required libraries -import pandas as pd import numpy as npCreate a multi-index with some NaN values. The names parameter sets the names for the levels in the index −multiIndex = pd.MultiIndex.from_arrays([[5, 10], [np.nan, 20], [25, np.nan], [35, 40]], names=['a', 'b', 'c', 'd'])Drop the value when any level is NaN in a Multi-index. Even with a single NaN value, the dropna() will drop all the values. The "how" parameter of the dropna() is used with ...
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