Use Pandas to Calculate Statistics in Python
Performing various complex statistical operations in python can be easily reduced to single line commands using pandas. We will discuss some of the most useful and common statistical operations in this post. We will be using the Titanic survival dataset to demonstrate such operations.
Python3
import pandas as pd
dataset = pd.read_csv( 'train.csv' )
dataset.head()
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Output:
1. Mean:
Calculates the mean or average value by using DataFrame/Series.mean() method.
Syntax: DataFrame/Series.mean(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters:
- axis: {index (0), columns (1)}
Specify the axis for the function to be applied on.
- skipna: This parameter takes bool value, default value is True
It excludes null values when computing the result.
- level: This parameter takes int value or level name, default value is None.
If the axis is a MultiIndex, count along a particular level, collapsing into a Series.
- numeric_only: This parameter takes bool value, default value is None
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data values. Not implemented for Series.
- **kwargs: Additional arguments to be passed to the function.
Returns: Mean of Series or DataFrame (if level specified)
Code:
Python3
mean = dataset[ 'Age' ].mean()
print (mean)
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Output:
29.69911764705882
2. Median:
Calculates the median value by using DataFrame/Series.median() method.
Syntax: DataFrame/Series.median(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters:
- axis: {index (0), columns (1)}
Specify the axis for the function to be applied on.
- skipna: This parameter takes bool value, default value is True
It excludes null values when computing the result.
- level: This parameter takes int or level name, default None
If the axis is a MultiIndex, count along a particular level, collapsing into a Series.
- numeric_only: This parameter takes bool value, default value is None
Include only float, int, boolean columns. If value is None, will attempt to use everything, then use only numeric data.
- **kwargs: Additional arguments to be passed to the function.
Returns: Median of Series or DataFrame (if level specified)
Code:
Python3
median = dataset[ 'Fare' ].median()
print (median)
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Output:
14.4542
3. Mode:
Calculates the mode or most frequent value by using DataFrame.mode() method.
Syntax: DataFrame/Series.mode(self, axis=0, numeric_only=False, dropna=True)
Parameters:
- axis: {index (0), columns (1)}
The axis to iterate over while searching for the mode value:
0 value or ‘index’ : get mode of each column
1 value or ‘columns’ : get mode of each row.
- numeric_only: This parameter takes bool value, default value is False.
If True, only apply to numeric value columns.
- dropna: This parameter takes bool value, default value is True.
Don’t consider counts of NaN/None value.
Returns: Highest frequency value.
Code:
Python3
mode = dataset[ 'Sex' ].mode()
print (mode)
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Output:
0 male
dtype: object
4. Count:
Calculates the count or frequency of non-null values by using DataFrame/Series.count() Method.
Syntax: DataFrame/Series.count(self, axis=0, level=None, numeric_only=False)
Parameters:
- axis: {0 or ‘index’, 1 or ‘columns’}, default value is 0
If value is 0 or ‘index’ counts are generated for each column. If value is 1 or ‘columns’ counts are generated for each row.
- level: (optional)This parameter takes int or str value.
If the axis is a MultiIndex type, count along a particular level, collapsing into a DataFrame. A str is used specifies the level name.
- numeric_only: This parameter takes bool value, default False
Include only float, int or boolean data.Returns: Return the highest frequency value
Returns: For each column/row the number of non-null entries. If level is specified returns a DataFrame structure.
Code:
Python3
count = dataset[ 'Ticket' ].count()
print (count)
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Output:
891
5. Standard Deviation:
Calculates the standard deviation of values by using DataFrame/Series.std() method.
Syntax: DataFrame/Series.std(self, axis=None, skipna=None, level=None, ddof=1, numeric_only=None, **kwargs)
Parameters:
- axis: {index (0), columns (1)}
- skipna: This parameters takes bool value, default value is True.
Exclude NA/null values. If an entire row/column has NA values, the result will be NA value.
- level: This parameters takes int or level name, default value is None.
If the axis is a MultiIndex, count along a particular level, collapsing into a Series.
- ddof: This parameter take int value, default value is 1.
Delta Degrees of Freedom. The divisor used in calculations is N – ddof, where N value represents the number of elements.
- numeric_only: This parameter takes bool value , default None
Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.
Returns: Standard Deviation
Code:
Python3
std = dataset[ 'Fare' ].std()
print (std)
|
Output:
49.693428597180905
6. Max:
Calculates the maximum value using DataFrame/Series.max() method.
Syntax: DataFrame/Series.max(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters:
- axis: {index (0), columns (1)}
Specify the axis for the function to be applied on.
- skipna: bool, default True
It excludes null values when computing the result.
- level: int or level name, default None
If the axis is a MultiIndex type, count along a particular level, collapsing into a Series.
- numeric_only: bool, default None
Include only float, int, boolean columns. If None value, will attempt to use everything, then use only numeric data.
- **kwargs: Additional keyword to be passed to the function.
Returns: Maximum value in Series or DataFrame (if level specified)
Code:
Python3
maxValue = dataset[ 'Age' ]. max ()
print (maxValue)
|
Output:
80.0
7. Min:
Calculates the minimum value using DataFrame/Series.min() method.
Syntax: DataFrame/Series.min(self, axis=None, skipna=None, level=None, numeric_only=None, **kwargs)
Parameters:
- axis: {index (0), columns (1)}
Specify the axis for the function to be applied on.
- skipna: bool, default True
It excludes null values when computing the result.
- level: int or level name, default None
If the axis is a MultiIndex type, count along a particular level, collapsing into a Series.
- numeric_only: bool, default None
Include only float, int, boolean columns. If None value, will attempt to use everything, then use only numeric data.
- **kwargs: Additional keyword to be passed to the function.
Returns: Minimum value in Series or DataFrame (if level specified)
Code:
Python3
minValue = dataset[ 'Fare' ]. min ()
print (minValue)
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Output:
0.0000
8. Describe:
Summarizes general descriptive statistics using DataFrame/Series.describe() method.
Syntax: DataFrame/Series.describe(self: ~ FrameOrSeries, percentiles=None, include=None, exclude=None)
Parameters:
- percentiles: list-like of numbers, optional
- include: ‘all’, list-like of dtypes or None values (default), optional
- exclude: list-like of dtypes or None values (default), optional,
Returns: Summary statistics of the Series or Dataframe provided.
Output:
Last Updated :
23 Jun, 2021
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