24 Jan Pandas – Statistical Functions
In this lesson, we will work around statistics operations using the statistical functions in Python Pandas. It can be applied to a Series or DataFrame.
- sum(): Return the sum of the values.
- count(): Return the count of non-empty values.
- max(): Return the maximum of the values.
- min(): Return the minimum of the values.
- mean(): Return the mean of the values.
- median(): Return the median of the values.
- std(): Return the standard deviation of the values.
- describe(): Return the summary statistics for each column.
Pandas sum() method
The sum() method in Python Pandas is used to return the sum of the values. Let us see an example:
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import pandas as pd # Dataset data = { 'Maths': [90, 85, 98, 80, 55, 78], 'Science': [92, 87, 59, 64, 87, 96], 'English': [95, 94, 84, 75, 67, 65] } # DataFrame df = pd.DataFrame(data) # Display the DataFrame print("DataFrame = \n",df) # Display the Sum of Marks in each column print("\nSum = \n",df.sum()) |
Output
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DataFrame = Maths Science English 0 90 92 95 1 85 87 94 2 98 59 84 3 80 64 75 4 55 87 67 5 78 96 65 Mean = Maths 486 Science 485 English 480 dtype: int64 |
Pandas count() method
The count() method in Python Pandas is used to return the count of non-empty values. Let us see an example:
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import pandas as pd # Dataset data = { 'Maths': [90, 85, 98, None, 55, 78], 'Science': [92, 87, 59, None, None, 96], 'English': [95, None, 84, 75, 67, None] } # DataFrame df = pd.DataFrame(data) # Display the DataFrame print("DataFrame = \n",df) # Display the Count of non-empty values in each column print("\nCount of non-empty values = \n",df.count()) |
Output
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DataFrame = Maths Science English 0 90.0 92.0 95.0 1 85.0 87.0 NaN 2 98.0 59.0 84.0 3 NaN NaN 75.0 4 55.0 NaN 67.0 5 78.0 96.0 NaN Count of non-empty values = Maths 5 Science 4 English 4 dtype: int64 |
Pandas max() method
The max() method in Python Pandas is used to return the maximum of the values. Let us see an example:
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import pandas as pd # Dataset data = { 'Maths': [90, 85, 98, 80, 55, 78], 'Science': [92, 87, 59, 64, 87, 96], 'English': [95, 94, 84, 75, 67, 65] } # DataFrame df = pd.DataFrame(data) # Display the DataFrame print("DataFrame = \n",df) # Display the Maximum of Marks in each column print("\nMaximum Marks = \n",df.max()) |
Output
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DataFrame = Maths Science English 0 90 92 95 1 85 87 94 2 98 59 84 3 80 64 75 4 55 87 67 5 78 96 65 Maximum Marks = Maths 98 Science 96 English 95 dtype: int64 |
Pandas min() method
The min() method in Python Pandas is used to return the minimum of the values. Let us see an example:
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import pandas as pd # Dataset data = { 'Maths': [90, 85, 98, 80, 55, 78], 'Science': [92, 87, 59, 64, 87, 96], 'English': [95, 94, 84, 75, 67, 65] } # DataFrame df = pd.DataFrame(data) # Display the DataFrame print("DataFrame = \n",df) # Display the Minimum of Marks in each column print("\nMinimum Marks = \n",df.min()) |
Output
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DataFrame = Maths Science English 0 90 92 95 1 85 87 94 2 98 59 84 3 80 64 75 4 55 87 67 5 78 96 65 Minimum Marks = Maths 55 Science 59 English 65 dtype: int64 |
Pandas mean() method
The mean() method in Python Pandas is used to return the mean of the values. Let us see an example:
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import pandas as pd # Dataset data = { 'Maths': [90, 85, 98, 80, 55, 78], 'Science': [92, 87, 59, 64, 87, 96], 'English': [95, 94, 84, 75, 67, 65] } # DataFrame df = pd.DataFrame(data) # Display the DataFrame print("DataFrame = \n",df) # Display the Mean of Marks in each column print("\nMean = \n",df.mean()) |
Output
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DataFrame = Maths Science English 0 90 92 95 1 85 87 94 2 98 59 84 3 80 64 75 4 55 87 67 5 78 96 65 Mean = Maths 81.000000 Science 80.833333 English 80.000000 dtype: float64 |
Pandas median() method
The median() method in Python Pandas is used to return the median of the values. Let us see an example:
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import pandas as pd # Dataset data = { 'Maths': [90, 85, 98, 80, 55, 78], 'Science': [92, 87, 59, 64, 87, 96], 'English': [95, 94, 84, 75, 67, 65] } # DataFrame df = pd.DataFrame(data) # Display the DataFrame print("DataFrame = \n",df) # Display the Median of Marks in each column print("\nMedian = \n",df.median()) |
Output
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DataFrame = Maths Science English 0 90 92 95 1 85 87 94 2 98 59 84 3 80 64 75 4 55 87 67 5 78 96 65 Median = Maths 82.5 Science 87.0 English 79.5 dtype: float64 |
Pandas std() method
The std() method in Python Pandas is used to return the standard deviation of the values. Let us see an example:
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import pandas as pd # Dataset data = { 'Maths': [90, 85, 98, 80, 55, 78], 'Science': [92, 87, 59, 64, 87, 96], 'English': [95, 94, 84, 75, 67, 65] } # DataFrame df = pd.DataFrame(data) # Display the DataFrame print("DataFrame = \n",df) # Display the Standard Deviation of Marks in each column print("\nStandard Deviation = \n",df.std()) |
Output
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DataFrame = Maths Science English 0 90 92 95 1 85 87 94 2 98 59 84 3 80 64 75 4 55 87 67 5 78 96 65 Standard Deviation = Maths 14.642404 Science 15.432649 English 13.084342 dtype: float64 |
Pandas describe() method
The describe() method in Python Pandas is used to return the summary statistics for each column. Let us see an example:
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import pandas as pd # Dataset data = { 'Maths': [90, 85, 98, None, 55, 78], 'Science': [92, 87, 59, None, None, 96], 'English': [95, None, 84, 75, 67, None] } # DataFrame df = pd.DataFrame(data) # Display the DataFrame print("DataFrame = \n",df) # Display the summary using the describe() method print("\nSummary of Statistics = \n",df.describe()) |
Output
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DataFrame = Maths Science English 0 90.0 92.0 95.0 1 85.0 87.0 NaN 2 98.0 59.0 84.0 3 NaN NaN 75.0 4 55.0 NaN 67.0 5 78.0 96.0 NaN Sumamry of Statistics = Maths Science English count 5.00000 4.000000 4.000000 mean 81.20000 83.500000 80.250000 std 16.36154 16.743158 12.038134 min 55.00000 59.000000 67.000000 25% 78.00000 80.000000 73.000000 50% 85.00000 89.500000 79.500000 75% 90.00000 93.000000 86.750000 max 98.00000 96.000000 95.000000 |
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