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‎09-DescriptiveStatistics.md

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```python
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Python Pandas - Descriptive Statistics
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=======================================
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A large number of methods collectively compute descriptive statistics and other related operations on DataFrame.
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Most of these are aggregations like sum(), mean(), but some of them, like sumsum(), produce an object of the
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same size. Generally speaking, these methods take an axis argument, just like ndarray.{sum, std, ...}, but the axis can be
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specified by name or integer DataFrame − “index” (axis=0, default), “columns” (axis=1)
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Let us create a DataFrame and use this object throughout this chapter for all the operations.
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```
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```python
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Example
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```
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### Example
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```python
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import pandas as pd
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import numpy as np
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#Create a Dictionary of series
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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])}
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d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack', 'Lee','David','Gasper','Betina','Andres']),
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'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
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'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])}
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#Create a DataFrame
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df = pd.DataFrame(d)
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print( df )
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import pandas as pd
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import numpy as np
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#Create a Dictionary of series
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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])}
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d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack', 'Lee','David','Gasper','Betina','Andres']),
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'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
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'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])}
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#Create a DataFrame
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df = pd.DataFrame(d)
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print( df.sum() )
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dtype: float64
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```python
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mean()
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### ``mean()``
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Returns the average value
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```
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```python
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import pandas as pd
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import numpy as np
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#Create a Dictionary of series
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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])}
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d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack', 'Lee','David','Gasper','Betina','Andres']),
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'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
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'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])}
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#Create a DataFrame
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df = pd.DataFrame(d)
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print(df.mean())
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import pandas as pd
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import numpy as np
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#Create a Dictionary of series
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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])}
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d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack', 'Lee','David','Gasper','Betina','Andres']),
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'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
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'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])}
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#Create a DataFrame
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df = pd.DataFrame(d)
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print(df.std() )
@@ -152,15 +152,16 @@ Let us now understand the functions under Descriptive Statistics in Python Panda
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```python
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The following table list down the important functions − S.No. Function Description
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The following table list down the important functions
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1. count() Number of non-null observations
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2. sum() Sum of values
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3. mean() Mean of Values
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4. median() Median of Values
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5. mode() Mode of values
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6. std() Standard Deviation of the Values
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7.min() Minimum Value
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8. max() Maximum Value
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7. min() Minimum Value
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8. max() Maximum Value
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9. abs() Absolute Value
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10. prod() Product of Values
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11. cumsum() Cumulative Sum
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```python
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import pandas as pd import numpy as np
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#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])}
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#Create a DataFrame df = pd.DataFrame(d)
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print df.describe()
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import pandas as pd
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import numpy as np
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#Create a Dictionary of series
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d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack', 'Lee','David','Gasper','Betina','Andres']),
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'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
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'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])}
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#Create a DataFrame
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df = pd.DataFrame(d)
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print(df.describe())
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```
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Now, use the following statement in the program and check the output − import pandas as pd import numpy as np
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#Create a Dictionary of series
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```
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```python
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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])}
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#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
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import pandas as pd
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import numpy as np
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#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])}
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#Create a DataFrame df = pd.DataFrame(d)
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print df. describe(include='all')
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#Create a Dictionary of series
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d = {'Name':pd.Series(['Tom','James','Ricky','Vin','Steve','Smith','Jack', 'Lee','David','Gasper','Betina','Andres']),
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'Age':pd.Series([25,26,25,23,30,29,23,34,40,30,51,46]),
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'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])}
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#Create a DataFrame
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df = pd.DataFrame(d)
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print(df. describe(include='all') )
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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
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```
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Age Name Rating
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count 12.000000 12 12.000000
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unique NaN 12 NaN
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top NaN Ricky NaN
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freq NaN 1 NaN
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mean 31.833333 NaN 3.743333
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std 9.232682 NaN 0.661628
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min 23.000000 NaN 2.560000
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25% 25.000000 NaN 3.230000
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50% 29.500000 NaN 3.790000
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75% 35.500000 NaN 4.132500
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max 51.000000 NaN 4.800000
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‎12-iteration.md

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y
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```python
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To iterate over the rows of the DataFrame, we can use the following functions −
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iteritems() − to iterate over the (key,value) pairs
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iterrows() − iterate over the rows as (index,series) pairs
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itertuples() − iterate over the rows as namedtuples
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iteritems()
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Iterates over each column as key, value pair with label as key and column value as a Series object.
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```
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```python
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* ``iteritems()`` - to iterate over the (key,value) pairs
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* ``iterrows()`` - iterate over the rows as (index,series) pairs
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* ``itertuples()`` - iterate over the rows as namedtuples
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* ``iteritems()`` - Iterates over each column as key, value pair with label as key and column value as a Series object.
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```python
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import pandas as pd
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import numpy as np
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df = pd.DataFrame(np.random.randn(4,3),columns=['col1','col2','col3'])
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for key,value in df.iteritems():
64-
print key,value
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Its output is as follows −
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col1 0 0.802390
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1 0.324060
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2 0.256811
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3 0.839186
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Name: col1, dtype: float64
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col2 0 1.624313
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1 -1.033582
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2 1.796663
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3 1.856277
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Name: col2, dtype: float64
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col3 0 -0.022142
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1 -0.230820
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2 1.160691
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Name: col3, dtype: float64
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print(key,value)
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```
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col1 0 1.141317
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1 0.289031
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2 -1.269689
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3 -1.668425
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Name: col1, dtype: float64
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col2 0 1.561011
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1 0.391033
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2 0.083089
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3 0.106299
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Name: col2, dtype: float64
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col3 0 -0.436407
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1 0.136565
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2 0.444321
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3 0.738629
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Name: col3, dtype: float64
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```python
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df = pd.DataFrame(np.random.randn(4,3),columns = ['col1','col2','col3'])
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for row_index,row in df.iterrows():
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print row_index,row
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Its output is as follows −
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0 col1 1.529759
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col2 0.762811
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col3 -0.634691
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Name: 0, dtype: float64
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1 col1 -0.944087
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col2 1.420919
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col3 -0.507895
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Name: 1, dtype: float64
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2 col1 -0.077287
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col2 -0.858556
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col3 -0.663385
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Name: 2, dtype: float64
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3 col1 -1.638578
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col2 0.059866
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Name: 3, dtype: float64
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print("\n")
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print(row_index,row)
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```
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0 col1 -0.469367
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col2 -1.466803
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col3 0.493435
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Name: 0, dtype: float64
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1 col1 0.686016
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col2 -1.293819
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col3 -1.087791
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Name: 1, dtype: float64
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2 col1 0.646084
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col2 -0.312096
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col3 1.518408
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Name: 2, dtype: float64
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3 col1 -2.464781
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col2 0.211235
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col3 -0.238992
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Name: 3, dtype: float64
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Note − Because iterrows() iterate over the rows, it doesn't preserve the data type across the row. 0,1,2 are the row indices and col1,col2,col3 are column indices.
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```
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```python
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itertuples()
128131
itertuples() method will return an iterator yielding a named tuple for each row in the DataFrame. The first element of the tuple will be the row’s corresponding index value, while the remaining values are the row values.
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```
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```python
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import pandas as pd
130139
import numpy as np
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df = pd.DataFrame(np.random.randn(4,3),columns = ['col1','col2','col3'])
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for row in df.itertuples():
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print row
143+
print("\n")
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print(row)
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```
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Pandas(Index=0, col1=-0.9771341226396765, col2=0.2724475615802741, col3=-0.6589499024186599)
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Pandas(Index=1, col1=1.6177467086432253, col2=-0.9763868574908899, col3=0.08317561529190409)
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Pandas(Index=2, col1=-0.988445247281908, col2=-0.6366889592765412, col3=0.3956289433362847)
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Pandas(Index=3, col1=-0.19595952598665276, col2=-0.13115863172857256, col3=-0.04519796025813786)
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```python
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Its output is as follows −
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Pandas(Index=0, col1=1.5297586201375899, col2=0.76281127433814944, col3=-
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0.6346908238310438)
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Pandas(Index=1, col1=-0.94408735763808649, col2=1.4209186418359423, col3=-
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0.50789517967096232)
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Pandas(Index=2, col1=-0.07728664756791935, col2=-0.85855574139699076, col3=-
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0.6633852507207626)
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Pandas(Index=3, col1=0.65734942534106289, col2=-0.95057710432604969,
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col3=0.80344487462316527)
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Note − Do not try to modify any object while iterating. Iterating is meant for reading and the iterator returns a copy of the original object (a view), thus the changes will not reflect on the original object.
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```
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```python
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df = pd.DataFrame(np.random.randn(4,3),columns = ['col1','col2','col3'])
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for index, row in df.iterrows():
166-
row['a'] = 10
167-
print df
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Its output is as follows −
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col1 col2 col3
170-
0 -1.739815 0.735595 -0.295589
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1 0.635485 0.106803 1.527922
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2 -0.939064 0.547095 0.038585
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3 -1.016509 -0.116580 -0.523158
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row['a'] = 10
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print(df)
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```
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col1 col2 col3
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0 0.325979 0.892602 -1.034127
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1 2.267333 -0.356288 -2.088448
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2 -1.159300 1.004701 0.742375
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3 0.132715 -1.565420 -1.142597
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```python
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Observe, no changes reflected.
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```

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