The describe() method is used for calculating some statistical data like percentile, mean and std of the numerical values of the Series or DataFrame. It analyzes both numeric and object series and also the DataFrame column sets of mixed data types.
Syntax
- DataFrame.describe(percentiles=None, include=None, exclude=None)
Parameters
- percentile: It is an optional parameter which is a list like data type of numbers that should fall between 0 and 1. Its default value is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
- include: It is also an optional parameter that includes the list of the data types while describing the DataFrame. Its default value is None.
- exclude: It is also an optional parameter that exclude the list of data types while describing DataFrame. Its default value is None.
Returns
It returns the statistical summary of the Series and DataFrame.
Example
import pandas as pd import numpy as np a1 = pd.Series([1, 2, 3]) a1.describe()
Output
count 3.0 mean 2.0 std 1.0 min 1.0 25% 1.5 50% 2.0 75% 2.5 max 3.0 dtype: float64
Example
import pandas as pd import numpy as np a1 = pd.Series(['p', 'q', 'q', 'r']) a1.describe()
OutputException Handling in Java – Javatpoint
count 4 unique 3 top q freq 2 dtype: object
Example
import pandas as pd
import numpy as np
a1 = pd.Series([1, 2, 3])
a1.describe()
a1 = pd.Series(['p', 'q', 'q', 'r'])
a1.describe()
info = pd.DataFrame({'categorical': pd.Categorical(['s','t','u']),
'numeric': [1, 2, 3],
'object': ['p', 'q', 'r']
})
info.describe(include=[np.number])
info.describe(include=[np.object])
info.describe(include=['category'])
Output
categorical count 3 unique 3 top u freq 1
Example
import pandas as pd
import numpy as np
a1 = pd.Series([1, 2, 3])
a1.describe()
a1 = pd.Series(['p', 'q', 'q', 'r'])
a1.describe()
info = pd.DataFrame({'categorical': pd.Categorical(['s','t','u']),
'numeric': [1, 2, 3],
'object': ['p', 'q', 'r']
})
info.describe()
info.describe(include='all')
info.numeric.describe()
info.describe(include=[np.number])
info.describe(include=[np.object])
info.describe(include=['category'])
info.describe(exclude=[np.number])
info.describe(exclude=[np.object])
Output
categorical numeric count 3 3.0 unique 3 NaN top u NaN freq 1 NaN mean NaN 2.0 std NaN 1.0 min NaN 1.0 25% NaN 1.5 50% NaN 2.0 75% NaN 2.5 max NaN 3.0
A large number of methods collectively compute descriptive statistics and other related operations on DataFrame. Most of these are aggregations like sum(), mean(), but some of them, like sumsum(), produce an object of the same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, …}, but the axis can be specified by name or integer
- DataFrame − “index” (axis=0, default), “columns” (axis=1)
Let us create a DataFrame and use this object throughout this chapter for all the operations.
Example
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','David','Gasper','Betina','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
#Create a DataFrame
df = pd.DataFrame(d)
print df
Its output is as follows −
Age Name Rating 0 25 Tom 4.23 1 26 James 3.24 2 25 Ricky 3.98 3 23 Vin 2.56 4 30 Steve 3.20 5 29 Smith 4.60 6 23 Jack 3.80 7 34 Lee 3.78 8 40 David 2.98 9 30 Gasper 4.80 10 51 Betina 4.10 11 46 Andres 3.65
sum()
Returns the sum of the values for the requested axis. By default, axis is index (axis=0).
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','David','Gasper','Betina','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
#Create a DataFrame
df = pd.DataFrame(d)
print df.sum()
Its output is as follows −
Age 382 Name TomJamesRickyVinSteveSmithJackLeeDavidGasperBe... Rating 44.92 dtype: object
Each individual column is added individually (Strings are appended).
axis=1
This syntax will give the output as shown below.
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','David','Gasper','Betina','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
#Create a DataFrame
df = pd.DataFrame(d)
print df.sum(1)
Its output is as follows −
0 29.23 1 29.24 2 28.98 3 25.56 4 33.20 5 33.60 6 26.80 7 37.78 8 42.98 9 34.80 10 55.10 11 49.65 dtype: float64
mean()
Returns the average value
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','David','Gasper','Betina','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
#Create a DataFrame
df = pd.DataFrame(d)
print df.mean()
Its output is as follows −
Age 31.833333 Rating 3.743333 dtype: float64
std()
Returns the Bressel standard deviation of the numerical columns.
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','David','Gasper','Betina','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
#Create a DataFrame
df = pd.DataFrame(d)
print df.std()
Its output is as follows −
Age 9.232682 Rating 0.661628 dtype: float64
Functions & Description
Let us now understand the functions under Descriptive Statistics in Python Pandas. The following table list down the important functions −
| Sr.No. | Function | Description |
|---|---|---|
| 1 | count() | Number of non-null observations |
| 2 | sum() | Sum of values |
| 3 | mean() | Mean of Values |
| 4 | median() | Median of Values |
| 5 | mode() | Mode of values |
| 6 | std() | Standard Deviation of the Values |
| 7 | min() | Minimum Value |
| 8 | max() | Maximum Value |
| 9 | abs() | Absolute Value |
| 10 | prod() | Product of Values |
| 11 | cumsum() | Cumulative Sum |
| 12 | cumprod() | Cumulative Product |
Note − Since DataFrame is a Heterogeneous data structure. Generic operations don’t work with all functions.
- Functions like sum(), cumsum() work with both numeric and character (or) string data elements without any error. Though n practice, character aggregations are never used generally, these functions do not throw any exception.
- Functions like abs(), cumprod() throw exception when the DataFrame contains character or string data because such operations cannot be performed.
Summarizing Data
The describe() function computes a summary of statistics pertaining to the DataFrame columns.
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','David','Gasper','Betina','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
#Create a DataFrame
df = pd.DataFrame(d)
print df.describe()
Its output is as follows −
Age Rating count 12.000000 12.000000 mean 31.833333 3.743333 std 9.232682 0.661628 min 23.000000 2.560000 25% 25.000000 3.230000 50% 29.500000 3.790000 75% 35.500000 4.132500 max 51.000000 4.800000
This function gives the mean, std and IQR values. And, function excludes the character columns and given summary about numeric columns. ‘include’ is the argument which is used to pass necessary information regarding what columns need to be considered for summarizing. Takes the list of values; by default, ‘number’.
- object − Summarizes String columns
- number − Summarizes Numeric columns
- all − Summarizes all columns together (Should not pass it as a list value)
Now, use the following statement in the program and check the output −
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','David','Gasper','Betina','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
#Create a DataFrame
df = pd.DataFrame(d)
print df.describe(include=['object'])
Its output is as follows −
Name count 12 unique 12 top Ricky freq 1
import pandas as pd
import numpy as np
#Create a Dictionary of series
d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack',
'Lee','David','Gasper','Betina','Andres']),
'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
'Rating':pd.Series([4.23,3.24,3.98,2.56,3.20,4.6,3.8,3.78,2.98,4.80,4.10,3.65])
}
#Create a DataFrame
df = pd.DataFrame(d)
print df. describe(include='all')
Its output is as follows −
Age Name Rating count 12.000000 12 12.000000 unique NaN 12 NaN top NaN Ricky NaN freq NaN 1 NaN mean 31.833333 NaN 3.743333 std 9.232682 NaN 0.661628 min 23.000000 NaN 2.560000 25% 25.000000 NaN 3.230000 50% 29.500000 NaN 3.790000 75% 35.500000 NaN 4.132500 max 51.000000 NaN 4.800000
