The ndim is an attribute in the pandas DataFrame which is used to get the integer/number representation of dimensions of the given DataFrame object.As we know, the pandas DataFrame is a two-dimensional data structure that is used to store the data in a tabular format. Regardless of the number of rows and columns lengths or type of data the dimensions of the DataFrame do not affect.The output for the ndim property of pandas DataFrame is always 2.Example 1In this following example, we have applied the ndim attribute to the pandas DataFrame object “df”, this DataFrame is created with a single ... Read More

The ndim is an attribute in the pandas series which is used to get the integer representation of dimensions of a series object.As we know, the pandas series is a 1-dimensional data structure so the output for this ndim property is always 1. It doesn’t take any input to get the dimensions. Regardless of the number of rows and columns, the ndim property always returns 1 for pandas Series.Example 1In this following example, we have applied the ndim attribute to the pandas series object “s”.# importing packages import pandas as pd import numpy as np # create pandas Series ... Read More

The loc is an attribute in the pandas DataFrame constructor that is used to access the elements of the DataFrame based on row/column label indexing.The attribute .loc takes the labels of the DataFrame row and column to access the group of elements.The “.loc” attribute allows inputs like an integer value, a list of integer values, and a slicing object with integers, and boolean array, etc. And it will raise a KeyError if the specified label is not found in the DataFrame.Example 1In this following example, we have applied the slicing indexer to the loc attribute to access the values from ... Read More

The “.loc” is an attribute of the pandas.DataFrame. it is used to access elements from DataFrame based on row/column label indexing. And It works similar to pandas.DataFrame “at” attribute but the difference is, the “at” attribute is used to access only a single element whereas the “loc” attribute can access a group of elements.The “.loc” attribute allows inputs like an integer value, a list of integer values, and a slicing object with integers, and boolean array, etc. And it will raise a KeyError if the specified label is not found in the DataFrame.Example 1In this following example, we created a ... Read More

The DataFrame is a pandas two-dimension data structure that is used to store the labeled data in a table format, a DataFrame has row index labels and column index labels which are used to represent the element (a value) address.By using these row/column labels we can access elements of a DataFrame and we can do data manipulations too.If you want to get the column labels from a DataFrame separately then we can use the pandas.DataFrame “columns” attribute.Example 1In this example, we have applied the columns attribute to the pandas DataFrame to get the column labels.# importing pandas package import pandas ... Read More

A DataFrame is a pandas data structure that is used to store the labeled data in a two-dimension, the labels can be anything like text data, integer values, and time sequence. by using these labels we can access elements of a given DataFrame and we can do data manipulations too.In pandas.DataFrame the row labels are called indexes, If you want to get index labels separately then we can use pandas.DataFrame “index” attribute.Example 1In this example, we have applied the index attribute to the pandas DataFrame to get the row index labels.# importing pandas package import pandas as pd # ... Read More

The pandas DataFrame.iloc is an attribute that is used to access the elements of the DataFrame using integer-location-based index values.The attribute .iloc only takes the integer values which are specifying the row and column index positions. Generally, the position-based index values are represented from 0 to length-1.Beyond this range only we can access the DataFrame elements otherwise it will raise an “IndexError”. But the slice indexer won’t raise “IndexError” for out-of-bound index value, because it allows out-of-bounds index values.Example 1In this following example, we have applied the slicing indexer to the iloc attribute to access the values from the 1st ... Read More

The pandas.DataFrame.iloc attribute is used to access elements from a pandas DataFrame using the integer position. And It is very similar to the pandas.DataFrame “iat” attribute but the difference is, the “iloc” attribute can access a group of elements whereas the “iat” attribute accesses only a single element.The “.iloc” attribute allows inputs like an integer value, a list of integer values, and a slicing object with integers, and boolean array, etc.The attribute will raise an “IndexError” if the requested index is out of bounds, except for the slicing indexer object.Example 1In this following example, we created a pandas DataFrame using ... Read More

The pandas.DataFrame.iat attribute is used to access a single value of the DataFrame using the row/column integer positions and It is very similar to the iloc in pandas instead of accessing a group of elements here we will access a single element.The “iat” attribute takes the integer index values of both rows and columns for getting or setting the element in a particular place.The attribute will raise an “IndexError” if the given integer position is out of bounds.Example 1In this following example, we have created a DataFrame, accessing the 2nd-row 1st column element by using the iat attribute.# importing pandas ... Read More

To check the data type in pandas DataFrame we can use the "dtype" attribute. The attribute returns a series with the data type of each column.And the column names of the DataFrame are represented as the index of the resultant series object and the corresponding data types are returned as values of the series object.If any column has mixed data types are stored then the data type of the entire column is indicated as object dtype.Example 1Apply the pandas dtype property and verify the data type of each in the DataFrame object.# importing pandas package import pandas as pd ... Read More