Pandas is built on top of NumPy, which means the Python pandas package depends on the NumPy package and also pandas intended with many other 3rd party libraries. So we can say that Numpy is required for operating the Pandas.The pandas library depends heavily on the Numpy array for the implementation of pandas data objects.Exampleimport pandas as pd df = pd.DataFrame({'A':[1, 2, 3, 4], 'B':[5, 6, 7, 8]}) print('Type of DataFrame: ', type(df)) print('Type of single Column A: ', type(df['A'])) print('Type of values in column A', type(df['A'].values)) print(df['A'].values)Explanationdf variable stores a DataFrame object created by using python ... Read More

To get the maximum value from Ordered CategoricalIndex, use the catIndex.max() method in Pandas.At first, import the required libraries −import pandas as pdSet the categories for the categorical using the "categories" parameter. Treat the categorical as ordered using the "ordered" parameter −catIndex = pd.CategoricalIndex(    ["p", "q", "r", "s", "p", "q", "r", "s"], ordered=True, categories=["p", "q", "r", "s"] )Display the Categorical Index −print("Categorical Index...", catIndex)Get the max value −print("Maximum value from CategoricalIndex...", catIndex.max())ExampleFollowing is the code −import pandas as pd # CategoricalIndex can only take on a limited, and usually fixed, number of possible values. # Set the categories ... Read More

To get the minimum value from Ordered CategoricalIndex, use the catIndex.min() method in Pandas. At first, import the required libraries −import pandas as pdSet the categories for the categorical using the "categories" parameter. Treat the categorical as ordered using the "ordered" parameter −catIndex = pd.CategoricalIndex(    ["p", "q", "r", "s", "p", "q", "r", "s"], ordered=True, categories=["p", "q", "r", "s"] )Display the Categorical Index −print("Categorical Index...", catIndex)Get the min value −print("Minimum value from CategoricalIndex...", catIndex.min())ExampleFollowing is the code −import pandas as pd # CategoricalIndex is the Index based on an underlying Categorical # Set the categories for the categorical using ... Read More

To create an Index based on an underlying Categorical, use the pandas.CategoricalIndex() method.At first, import the required libraries −import pandas as pdCategoricalIndex is the Index based on an underlying Categorical. CategoricalIndex can only take on a limited, and usually fixed, number of possible values. Set the categories for the categorical using the "categories" parameter. Treat the categorical as ordered using the "ordered" parameter −catIndex = pd.CategoricalIndex(    ["p", "q", "r", "s", "p", "q", "r", "s"], ordered=True, categories=["p", "q", "r", "s"] )Display the Categorical Index −print("Categorical Index...", catIndex)Get the categories −print("DisplayingCategories from CategoricalIndex...", catIndex.categories)ExampleFollowing is the code −import pandas as pd ... Read More

To create RangeIndex from a range object, use the pd.RangeIndex.from_range(range()) method in Pandas.At first, import the required libraries −import pandas as pdCreate RangeIndex −index = pd.RangeIndex.from_range(range(10, 30)) Display the RangeIndex −print("RangeIndex...",index)ExampleFollowing is the code −import pandas as pd # RangeIndex index = pd.RangeIndex.from_range(range(10, 30)) # Display the RangeIndex print("RangeIndex...",index) # Display the start value print("RangeIndex start value...",index.start) # Display the end value print("RangeIndex end value...",index.stop)OutputThis will produce the following output −RangeIndex... RangeIndex(start=10, stop=30, step=1) RangeIndex start value... 10 RangeIndex end value... 30

To display the value of the step parameter of RangeIndex, use the index.step property in Pandas.At first, import the required libraries −import pandas as pdRangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. Create a range index with start, stop and step. The name is the name to be stored in the index.index = pd.RangeIndex(start=10, stop=30, step=2, name="data") Display the step parameter value −print("RangeIndex step value...", index.step)ExampleFollowing is the code −import pandas as pd # RangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. # Create a range index with start, ... Read More

To display the value of the stop parameter of RangeIndex, use the index.stop property in Pandas.At first, import the required libraries −import pandas as pdRangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. Create a range index with start, stop and step. The name is the name to be stored in the index.index = pd.RangeIndex(start=5, stop=20, step=2, name="data") Display the RangeIndex −print("RangeIndex...", index)Display the stop parameter value −print("RangeIndex stop value...", index.stop) ExampleFollowing is the code −import pandas as pd # RangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. # Using ... Read More

To display the value of the start parameter of RangeIndex, use the index.start property in Pandas. At first, import the required libraries −import pandas as pdRangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. Using RangeIndex may in some instances improve computing speed. Create a range index with start, stop and step. The name is the name to be stored in the index.index = pd.RangeIndex(start=5, stop=20, step=2, name="data") Display the RangeIndex −print("RangeIndex...", index)Display the start parameter value −print("RangeIndex start value...", index.start) ExampleFollowing is the code −import pandas as pd # Create a range index with ... Read More

To create a RangeIndex, use the pandas.RangeIndex() method in Pandas. At first, import the required libraries −import pandas as pdRangeIndex is a memory-saving special case of Int64Index limited to representing monotonic ranges. Using RangeIndex may in some instances improve computing speed.Create a range index with start, stop and step. The name is the name to be stored in the index −index = pd.RangeIndex(start=10, stop=30, step=2, name="data") Display the start parameter value −print("RangeIndex start value...", index.start)Display the stop parameter value −print("RangeIndex stop value...", index.stop) Display the step parameter value −print("RangeIndex step value...", index.step)ExampleFollowing is the code −import pandas as pd ... Read More

To compute slice locations for input labels, use the index.slice_locs() method. At first, import the required libraries −import pandas as pdCreate Pandas index object −index = pd.Index(list('pqrstuvwxyz')) Display the Pandas index −print("Pandas Index...", index)Get the slice locations. The "start" is the label to begin with. The "end" is the label to end withprint("The slice locations with start and stop...", index.slice_locs(start='q', end='v')) ExampleFollowing is the code −import pandas as pd # Create Pandas index object index = pd.Index(list('pqrstuvwxyz')) # Display the Pandas index print("Pandas Index...", index) # Return the number of elements in the Index print("Number of elements ... Read More