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Pandas Articles
Page 26 of 42
Python Pandas - Return a string of the type inferred from the values
To return a string of the type inferred from the values, use the index.inferred_type property in Pandas.At first, import the required libraries −import pandas as pd import numpy as npCreating the index. For NaN, we have used numpy library −index = pd.Index(['Car', 'Bike', np.nan, 'Car', np.nan, 'Ship', None, None]) Display the index −print("Pandas Index...", index)Return a string of the type inferred from the values −print("The inferred type...", index.inferred_type) ExampleFollowing is the code −import pandas as pd import numpy as np # Creating the index # For NaN, we have used numpy library index = pd.Index(['Car', 'Bike', np.nan, 'Car', np.nan, ...
Read MorePython Pandas - Return the dtype object of the underlying data
To return the dtype object of the underlying data, use the index.dtype property in Pandas.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index(['Car', 'Bike', 'Shop', 'Car', 'Airplace', 'Truck']) Display the index −print("Pandas Index...", index)Return the dtype of the data −print("The dtype object...", index.dtype) ExampleFollowing is the code −import pandas as pd # Creating the index index = pd.Index(['Car', 'Bike', 'Shop', 'Car', 'Airplace', 'Truck']) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Return the dtype of the data print("The ...
Read MorePython Pandas - Check if the index has NaNs
To check if the index has NaNs, use the index.hasnans property in Pandas.At first, import the required libraries −import pandas as pd import numpy as npCreating the index. For NaN, we have used numpy library −index = pd.Index(['Car', 'Bike', np.nan, 'Car', np.nan, 'Ship']) Display the index −print("Pandas Index...", index)Check if the index is having NaNs −print("Is the Pandas index having NaNs?", index.hasnans) ExampleFollowing is the code −import pandas as pd import numpy as np # Creating the index # For NaN, we have used numpy library index = pd.Index(['Car', 'Bike', np.nan, 'Car', np.nan, 'Ship']) # Display the ...
Read MorePython Pandas - Check if the index has duplicate values
To check if the index has duplicate values, use the index.has_duplicates property in Pandas.At first, import the required libraries −import pandas as pdCreating the index −index = pd.Index(['Car', 'Bike', 'Truck', 'Car', 'Airplane']) Display the index −print("Pandas Index...", index)Check if the index is having duplicate values −print("Is the Pandas index having duplicate values?", index.has_duplicates) ExampleFollowing is the code −import pandas as pd # Creating the index index = pd.Index(['Car', 'Bike', 'Truck', 'Car', 'Airplane']) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Check if the index ...
Read MorePython Pandas - Check if the index has unique values
To check if the index has unique values, use the index.is_unique.At first, import the required libraries −import pandas as pdLet us create the index −index = pd.Index([50, 40, 30, 20, 10]) Display the index −print("Pandas Index...", index)Check if the index is having unique values −print("Is the Pandas index having unique values?", index.is_unique) ExampleFollowing is the code −import pandas as pd # Creating the index index = pd.Index([50, 40, 30, 20, 10]) # Display the index print("Pandas Index...", index) # Return an array representing the data in the Index print("Array...", index.values) # Check if the index is ...
Read MorePython 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 - 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 - 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 - Return a new Timedelta ceiled to this resolution
To return a new Timedelta ceiled to this resolution, use the timedelta.ceil() method. With that, set the resolution using the freq parameter.At first, import the required libraries −import pandas as pdTimeDeltas is Python’s standard datetime library uses a different representation timedelta’s. Create a Timedelta objecttimedelta = pd.Timedelta('6 days 1 min 30 s') Display the Timedeltaprint("Timedelta...", timedelta)Return the ceiled Timestamp ceiled to days frequencyres = timedelta.ceil(freq='D') ExampleFollowing is the code import pandas as pd # TimeDeltas is Python’s standard datetime library uses a different representation timedelta’s # create a Timedelta object timedelta = pd.Timedelta('6 days 1 min 30 s') # ...
Read MorePython Pandas - Get the seconds from Timedelta object using string input
To return the seconds from Timedelta object, use the timedelta.seconds property. At first, import the required libraries −import pandas as pdTimeDeltas is Python’s standard datetime library uses a different representation timedelta’s. Set string input for seconds using unit 's'. Create a Timedelta objecttimedelta = pd.Timedelta('1 min 30 s') Display the Timedeltaprint("Timedelta...", timedelta)Return the seconds valuetimedelta.seconds ExampleFollowing is the code import pandas as pd # TimeDeltas is Python’s standard datetime library uses a different representation timedelta’s # set string input for seconds using unit 's' # create a Timedelta object timedelta = pd.Timedelta('1 min 30 s') # display the ...
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