30 Dec DataFrame – Attributes and Methods
The Pandas DataFrame is a Two-dimensional, tabular data, that uses the DataFrame() method to create a DataFrame. It also uses different built-in attributes and methods for basic functionalities. In this lesson, let us see such attributes and methods in Python Pandas for DataFrame:
- dtypes: Return the dtypes in the DataFrame
- ndim: Return the number of dimensions of the DataFrame
- size: Return the number of elements in the DataFrame.
- shape: Return the dimensionality of the DataFrame in the form of a tuple.
- index: Return the index of the DataFrame
- T: Transpose the rows and columns
- head(): Return the first n rows.
- tail(): Return the last n rows.
Let us understand them one by one:
dtypes
The pandas.DataFrame.dtypes is used to return the dtypes in the DataFrame.
Let us now see an example to implement the dtypes attribute in Python Pandas:
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import pandas as pd # Dataset data = { 'Student': ["Amit", "John", "Jacob", "David", "Steve"], 'Rank': [1, 4, 3, 5, 2], 'Marks': [95, 70, 80, 60, 90] } # Create a DataFrame using the DataFrame() method with index res = pd.DataFrame(data, index=['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], ) # Display the Records print("Student Records\n\n", res) # Datatypes in the DataFrame print("\nDatatypes:\n", res.dtypes) |
Output
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Student Records Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Steve 2 90 Datatypes: Student object Rank int64 Marks int64 dtype: object |
ndim
The pandas.DataFrame.ndim is used to return the number of dimensions of the DataFrame.
Let us now see an example to implement the ndim attribute in Python Pandas:
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import pandas as pd # Dataset data = { 'Student': ["Amit", "John", "Jacob", "David", "Steve"], 'Rank': [1, 4, 3, 5, 2], 'Marks': [95, 70, 80, 60, 90] } # Create a DataFrame using the DataFrame() method with index res = pd.DataFrame(data, index=['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], ) # Display the Records print("Student Records\n\n", res) # Number of Dimensions in the DataFrame print("\nNumber of Dimensions:\n", res.ndim) |
Output
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Student Records Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Steve 2 90 Number of Dimensions: 2 |
size
The pandas.DataFrame.size is used to return the number of elements in the DataFrame.
Let us now see an example to implement the size attribute in Python Pandas:
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import pandas as pd # Dataset data = { 'Student': ["Amit", "John", "Jacob", "David", "Steve"], 'Rank': [1, 4, 3, 5, 2], 'Marks': [95, 70, 80, 60, 90] } # Create a DataFrame using the DataFrame() method with index res = pd.DataFrame(data, index=['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], ) # Display the Records print("Student Records\n\n", res) # Number of elements in the DataFrame print("\nNumber of Elements:\n", res.size) |
Output
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Student Records Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Steve 2 90 Number of Elements: 15 |
shape
The pandas.DataFrame.shape is used to return the dimensionality of the DataFrame in the form of a tuple.
Let us now see an example to implement the shape attribute in Python Pandas:
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import pandas as pd # Dataset data = { 'Student': ["Amit", "John", "Jacob", "David", "Steve"], 'Rank': [1, 4, 3, 5, 2], 'Marks': [95, 70, 80, 60, 90] } # Create a DataFrame using the DataFrame() method with index res = pd.DataFrame(data, index=['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], ) # Display the Records print("Student Records\n\n", res) # Return the dimensionality of the DataFrame # Result in a Tuple form print("\nDimensionality:\n", res.shape) |
Output
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Student Records Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Steve 2 90 Dimensionality: (5, 3) |
index
The pandas.DataFrame.index is used to return the index of the DataFrame.
Let us now see an example to implement the index attribute in Python Pandas:
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import pandas as pd # Dataset data = { 'Student': ["Amit", "John", "Jacob", "David", "Steve"], 'Rank': [1, 4, 3, 5, 2], 'Marks': [95, 70, 80, 60, 90] } # Create a DataFrame using the DataFrame() method with index res = pd.DataFrame(data, index=['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], ) # Display the Records print("Student Records\n\n", res) # Return the index of the DataFrame print("\nDataFrame Index:\n", res.index) |
Output
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Student Records Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Steve 2 90 DataFrame Index: Index(['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], dtype='object') |
T
The pandas.DataFrame.T is used to Transpose the rows and columns.
Let us now see an example to implement the T attribute in Python Pandas:
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import pandas as pd # Dataset data = { 'Student': ["Amit", "John", "Jacob", "David", "Steve"], 'Rank': [1, 4, 3, 5, 2], 'Marks': [95, 70, 80, 60, 90] } # Create a DataFrame using the DataFrame() method with index res = pd.DataFrame(data, index=['RowA', 'RowB', 'RowC', 'RowD', 'RowE'], ) # Display the Records print("Student Records\n\n", res) # Return the Transpose print("\nTranspose:\n", res.T) |
Output
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Student Records Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Steve 2 90 Transpose: RowA RowB RowC RowD RowE Student Amit John Jacob David Steve Rank 1 4 3 5 2 Marks 95 70 80 60 90 |
head()
The pandas.DataFrame.head() is used to return the first n rows.
Let us now see an example to implement the head() method in Python Pandas:
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import pandas as pd # Dataset data = { 'Student': ["Amit", "John", "Jacob", "David", "Nathan", "Steve"], 'Rank': [1, 4, 3, 5, 6, 2], 'Marks': [95, 70, 80, 60, 55, 90] } # Create a DataFrame using the DataFrame() method with index res = pd.DataFrame(data, index=['RowA', 'RowB', 'RowC', 'RowD', 'RowE', 'RowF'], ) # Display the Records print("Student Records\n\n", res) # Return the first n rows # Default value of n is 5 print("\nFirst 5 rows:\n", res.head()) |
Output
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Student Records Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Nathan 6 55 RowF Steve 2 90 First 5 rows: Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Nathan 6 55 |
tail()
The pandas.DataFrame.tail() is used to return the last n rows.
Let us now see an example to implement the tail() method in Python Pandas:
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import pandas as pd # Dataset data = { 'Student': ["Amit", "John", "Jacob", "David", "Nathan", "Steve"], 'Rank': [1, 4, 3, 5, 6, 2], 'Marks': [95, 70, 80, 60, 55, 90] } # Create a DataFrame using the DataFrame() method with index res = pd.DataFrame(data, index=['RowA', 'RowB', 'RowC', 'RowD', 'RowE', 'RowF'], ) # Display the Records print("Student Records\n\n", res) # Return the last n rows # Default value of n is 5 print("\nLast 5 rows:\n", res.tail()) |
Output
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Student Records Student Rank Marks RowA Amit 1 95 RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Nathan 6 55 RowF Steve 2 90 Last 5 rows: Student Rank Marks RowB John 4 70 RowC Jacob 3 80 RowD David 5 60 RowE Nathan 6 55 RowF Steve 2 90 |
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