Python Pandas DataFrame

Pandas DataFrame in Python is a two dimensional data structure. It means, Pandas DataFrames stores data in a tabular format i.e., rows and columns. In this article, we will show you, how to create Python Pandas DataFrame, access dataFrame, alter DataFrame rows and columns. Next, we will discuss about Transposing DataFrame in Python, Iterating over DataFrame rows so on.

Creating a DataFrame in Python

In real-time, we use this Pandas dataFrame to load data from Sql Server, Text Files, Excel Files or any CSV Files. Next, we slice and dice that data as per our requirements. Once the data is in our required format, we use that data to create reports or charts or graphs using matplotlib module.

Pandas Create Empty DataFrame

This is a simple example to create a DataFrame in Python. For the demonstration purpose, we are creating an empty DataFrame

import pandas as pd
 
data = pd.DataFrame()
print(data)

OUTPUT

Pandas Create DataFrame from List

Here, we create a list of integer values. Next, as you can see, we used pandas.DataFrame function to create our DataFrame from list or to convert list to DataFrame.

import pandas as pd
 
table = [1, 2, 3, 4, 5]

data = pd.DataFrame(table)
print(data)

OUTPUT

This is an example of creating pandas DataFrame from Mixed List. Here, we are also using multiple rows and Columns

import pandas as pd
 
table = [[1, 'Suresh'], [2, 'Python'], [3, 'Hello']]

data = pd.DataFrame(table)
print(data)

OUTPUT

This example shows, how to assign the names to column values in a Pandas DataFrame. In the coming section, we will explain this one detail

import pandas as pd
 
table = [[1, 'Suresh'], [2, 'Python'], [3, 'Hello']]
data = pd.DataFrame(table, columns = ['S.No', 'Name'])
print(data)

OUTPUT

Python DataFrame of Random Numbers

This example shows, how to create a Pandas DataFrame using random numbers. Here, we used Numpy random function to generate random numbers of size 8 * 4. Next, we used DataFrame function to convert those sequence to a DataFrame

import numpy as np
import pandas as pd
 
d_frame = pd.DataFrame(np.random.randn(8, 4))
print(d_frame)

OUTPUT

Python Pandas DataFrame from dict

Python pandas allows you to create DataFrame from dict or dictionary. It was pretty much straight forward. All you have to do is, declare a dictionary of different values and then use Python DataFrame function to convert that dictionary to DataFrame

import pandas as pd
 
table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }
 
data = pd.DataFrame(table)
print(data)

TIP: In order to create a Pandas DataFrame from dict, the length of Dictionary values of all keys should be the same otherwise, it throws error. Next, if you are passing the index values then they should match the length of key values or arrays otherwise, it raises error. If you haven’t passed any index values then it will automatically create an index for you, and it start from 0 to n-1.

OUTPUT

Let me take another example of Python pandas DataFrame from Dictionary. This time, we are converting dictionary of four columns of Data to DataFrame.

import pandas as pd
table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }

data = pd.DataFrame(table)
print(data)

OUTPUT

Pandas create DataFrame from dict of lists

If you are confused to place everything in one place, you can divide them into part. Here, we declared four lists of items and then assigned them for each column.

import pandas as pd

names = ['John', 'Mike', 'Suresh', 'Tracy']
ages =  [25, 32, 30, 26]
Professions = ['Developer', 'Analyst', 'Admin', 'HR']
Salaries = [1000000, 1200000, 900000, 1100000]
	      
table = {'name': names,
         'Age': ages,
         'Profession': Professions,
         'Salary': Salaries
         }
	      
data = pd.DataFrame(table)
print(data)

OUTPUT

Python Pandas DataFrame of Dates

Using Python pandas module, you can also create a DataFrame with series of dates. Let me create a DataFrame of dates from 2019-01-01 to 2019-01-08. By changing the period values, you can generate more number of Date sequence.

import numpy as np
import pandas as pd

dates = pd.date_range('20190101', periods = 8)
print(dates)
print()

d_frame = pd.DataFrame(np.random.randn(8, 4), index = dates,
                       columns = {'apples', 'oranges', 'kiwis', 'bananas'})
print(d_frame)

OUTPUT

Pandas DataFrame Columns

This example show you, how to reorder the columns in a DataFrame. By default, DataFrame will use the column order that we used in the actual data. However, you can use the Columns argument to alter the position of any column. For example, let me change the Age from 2nd position to 4th.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }

data1 = pd.DataFrame(table)
print(data1)

print('\n---- After Changing the Column Order-----')
data2 = pd.DataFrame(table, columns = ['name', 'Profession', 'Salary', 'Age'])
print(data2)

Please be careful, while using this columns argument. If we specified any non-existing column name or typo mistake will returns NaN. Let me use Qualification column name (which doesn’t exist)

print('\n---- Using Wrong Column -----')
data3 = pd.DataFrame(table, columns = ['name', 'Qualification', 'Salary', 'Age'])
print(data3)

OUTPUT

The DataFrame columns attribute returns the list of available columns in a DataFrame in the same order, along with the datatype of a DataFrame

print(data1.columns)
print(data2.columns)
print(data3.columns)

OUTPUT

Pandas DataFrame Index

By default, Python will assign the index values from 0 to n-1, where n is the maximum number. However, you have an option to alter those default index values using the index attribute. Here, we using the same and assigning the alphabets from a to d as the index values.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }

#Without Index Values - Uses Default Values
data1 = pd.DataFrame(table)
print(data1)

# Index Values are a, b, c, d
data2 = pd.DataFrame(table, index = ['a', 'b', 'c', 'd'])
print('\n----After Setting Index Values----')
print(data2)

OUTPUT

In Python, you can use DataFrame set_index function to change or set a column as an index value. In this example, we are using this DataFrame set_index function not set name as an index. Next, we used loc function to show that, we can extra information using index name.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }
data = pd.DataFrame(table)
print(data)

print('\n---Setting name as an index---')
new_data = data.set_index('name')
print(new_data)

print('\n---Return Index John Details---')
print(new_data.loc['John'])

OUTPUT

Pandas DataFrame Attributes

The following are the list of available attributes of Python DataFrame

Python DataFrame shape attribute

The Pandas DataFrame shape attribute returns the shape or tuple of number of rows and columns in a DataFrame.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Salary':[1000000, 1200000, 900000, 1100000]
	    }

data = pd.DataFrame(table)
print(data)
print('\n---Shape or Size of a DataFrame---')
print(data.shape)

OUTPUT

Python DataFrame values attribute

The DataFrame values attributes returns the DataFrame data (without column names) in a two dimensional array format.

import pandas as pd
table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }

data2 = pd.DataFrame(table, columns = ['name', 'Profession', 'Salary', 'Age'])

data3 = pd.DataFrame(table, columns = ['name', 'Qualification', 'Salary', 'Age'])

print('---Data2 Values--- ')
print(data2.values)

print('\n---Data3 Values--- ')
print(data3.values)

OUTPUT

The above examples are returns an array of type Object. This is because, both these DataFrames has a mixed content (int, string). If that is not the case then it won’t display any dtype inside an array. To demonstrate the same, we used an integer DataFrame

import pandas as pd

table = {'Age': [25, 32, 30, 26],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }

data4 = pd.DataFrame(table)
print(data4.values)

OUTPUT

Pandas DataFrame name attribute

The Python DataFrame index and the column has a name attribute, which allows to assign a name to an index or column. 

import pandas as pd
table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }

data1 = pd.DataFrame(table)

table = {'Age': [25, 32, 30, 26],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }
data4 = pd.DataFrame(table)

data1.index.name = 'Emp No'
print(data1)
print()

data4.index.name = 'Cust No'
print(data4)

OUTPUT

Similarly, we can use columns name attribute to assign name for column headers.

data1.columns.name = 'Employee Details'
print(data1)
 
data4.columns.name = 'Customers Information'
print(data4)

OUTPUT

Python DataFrame dtype attribute

The DataFrame dtype attribute returns the data type of each column in a DataFrame.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Sale':[22.55, 12.66, 119.470, 200.190],
         'Salary':[10000, 12000, 9000, 11000]
         }

data = pd.DataFrame(table)
print(data)

print('\n---dtype attribute result---')
print(data.dtypes)

OUTPUT

Python DataFrame describe function

Use this python DataFrame describe function to get a quick statistical information about the DataFrame.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[10000, 12000, 9000, 11000]
         }

data1 = pd.DataFrame(table)
print(data1)

print('\n---describe function result---')
print(data1.describe())

OUTPUT

Access Python DataFrame Data

As we said earlier, data in Python DataFrame is stored in a tabular format of rows and columns. It means, you can access DataFrame items using columns and rows.

Access Pandas DataFrame Columns

You can access the DataFrame columns in two ways, either specifying the column name inside the [] or after a dot notation. Both these methods will returns the specified column as a Series.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[10000, 12000, 9000, 11000]
         }

data1 = pd.DataFrame(table)
data2 = pd.DataFrame(table, columns = ['name', 'Profession', 'Salary', 'Age'])

print('-----Accessing DataFrame Columns-----')
print(data1.Age)
print(data1['name'])
print(data2.Salary)

We can also access multiple DataFrame columns 
print('-----Accessing Multiple DataFrame Columns-----')
print(data1[['Age', 'Profession']])
print(data2[['name', 'Salary']])

OUTPUT

This is an another example to access pandas DataFrame columns

import pandas as pd
table = {'name': ['Kane', 'John', 'Suresh', 'Tracy', 'Steve'],
         'Age': [35, 25, 32, 30, 29],
         'Profession': ['Manager', 'Developer', 'Analyst', 'Admin', 'HR'],
         'Sale':[422.19, 22.55, 119.470, 200.190, 44.55],
         'Salary':[12000, 10000, 14000, 11000, 14000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---Select name column from DataFrame---')
print(data['name'])

print('\n---Select Profession and Sale column from DataFrame---')
print(data[['Profession', 'Sale']])

print('\n---Select Profession column from DataFrame---')
print(data.Profession)

OUTPUT

Access Pandas DataFrame Rows

A Pandas DataFrame in Python can also be accessed using rows. Here, we are using the index slicing technique to returns the required rows from a DataFrame. Here, data[1:] returns all the rows from index 1 to n-1, and data[1:3] returns rows from index 1 to 3.

import pandas as pd
table = {'name': ['Kane', 'John', 'Suresh', 'Tracy', 'Steve'],
         'Age': [35, 25, 32, 30, 29],
         'Profession': ['Manager', 'Developer', 'Analyst', 'Admin', 'HR'],
         'Sale':[422.19, 22.55, 119.470, 200.190, 44.55],
         'Salary':[12000, 10000, 14000, 11000, 14000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---Select all rows from 1 to N in a DataFrame---')
print(data[1:])

print('\n---Select rows from 1 to 2 in a DataFrame---')
print(data[1:3])

print('\n---Select rows from 0 to 3 in a DataFrame---')
print(data[0:4])

print('\n---Select last row in a DataFrame---')
print(data[-1:])

OUTPUT

Pandas DataFrame loc

A Pandas DataFrame loc is one of the important thing to understand. You can use the DataFrame loc[] to select more than one column and more than one row at a time. Or, use this Pandas dataFrame loc[] to select a portion of a DataFrame.

In this example, we are going to use this loc to select rows from a DataFrame in Python.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }
data = pd.DataFrame(table, index = ['a', 'b', 'c', 'd'])
print(data)

print('\n---Select b row from a DataFrame---')
print(data.loc['b'])

print('\n---Select c row from a DataFrame---')
print(data.loc['c'])

print('\n---Select b and d rows from a DataFrame---')
print(data.loc[['b', 'd']])

OUTPUT

The first statement, data.loc[:, [‘name’, ‘Sale’]] returns all the rows of name and sale column. Within the last statement, data.loc[1:3, [‘name’, ‘Profession’, ‘Salary’]] returns rows from index value 1 to 3 for the columns of name, profession and Salary.

import pandas as pd
table = {'name': ['Kane', 'John', 'Suresh', 'Tracy', 'Steve'],
         'Age': [35, 25, 32, 30, 29],
         'Profession': ['Manager', 'Developer', 'Analyst', 'Admin', 'HR'],
         'Sale':[422.19, 22.55, 119.470, 200.190, 44.55],
         'Salary':[12000, 10000, 14000, 11000, 14000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---Select name, Sale column in a DataFrame---')
print(data.loc[:, ['name', 'Sale']])

print('\n---Select name, Profession, Salary in a DataFrame---')
print(data.loc[:, ['name', 'Profession', 'Salary']])

print('\n---Select rows from 1 to 2 in a DataFrame---')
print(data.loc[1:3, ['name', 'Profession', 'Salary']])

OUTPUT

Pandas DataFrame iloc

Similar to loc[], Python Pandas DataFrame has iloc[]. However, this will only accept integer values or index to return data from a DataFrame.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }
data = pd.DataFrame(table, index = ['a', 'b', 'c', 'd'])
print(data)

print('\n---Select 1st row from a DataFrame---')
print(data.iloc[1])

print('\n---Select 3rd row from a DataFrame---')
print(data.iloc[3])

print('\n---Select 1 and 3 rows from a DataFrame---')
print(data.iloc[[1, 3]])

OUTPUT

You can use loc, iloc, at and iat to extract or access a single value from a DataFrame. The following example will show you the same.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }
data = pd.DataFrame(table)
print(data)

print('\nitem at 0, 0 in DataFrame   = ', data.iloc[0][0])

print('item at 0, 1 in DataFrame      = ', data.loc[0][1])

print('item at 1, Profession is       = ', data.loc[1]['Profession'])

print('item at 2, 3 in DataFrame      = ', data.iat[2, 3])

print('item at 0, Salary in DataFrame = ', data.at[0, 'Salary'])

OUTPUT

Add New Column to Pandas DataFrame

In this example, we will show you, how to add a new column to an existing DataFrame. data[‘Sale’] = [422.19, 200.190, 44.55] adds completely new column called Sale. data[‘Income’] = data[‘Salary’] + data[‘basic’] adds new column Income by adding values in Salary column and basic column.

import pandas as pd

table = {'name': ['Kane', 'Suresh', 'Tracy'],
         'Age': [35, 25, 29],
         'Profession': ['Manager', 'Developer', 'HR'],
         'Salary': [10000, 14000, 11000],
         'basic': [4000, 6000, 4500]
        }

data = pd.DataFrame(table)
print(data)

# Add New Column to DataFrame
data['Sale'] = [422.19, 200.190, 44.55]
print('\n---After adding New Column DataFrame---')
print(data)

# Add New Column using existing
data['Income'] = data['Salary'] + data['basic']
print('\n---Total Salary in a DataFrame---')
print(data)

# Add New Calculated Column to DataFrame
data['New_Salary'] = data['Salary'] + data['Salary'] * 0.25
print('\n---After adding New Column DataFrame---')
print(data)

OUTPUT

Delete Column from a DataFrame in Python

In Python, there are two ways to delete a column from a Pandas DataFrame. Either you can use del function or pop function. In this example, we are going to use both these function to delete columns from Pandas DataFrame.

Here, del(data[‘basic’]) deletes basic column (complete rows belong to basic column) from DataFrame. x = data.pop(‘Age’) deletes or pops Age column from DataFrame, and we are printing that popped column as well. Next, we used the Pandas DataFrame drop function to delete Sale column.

import pandas as pd

table = {'name': ['Kane', 'Suresh', 'Tracy'],
         'Age': [35, 25, 29],
         'Profession': ['Manager', 'Developer', 'HR'],
         'Salary': [10000, 14000, 11000],
         'basic': [4000, 6000, 4500],
         'Sale': [422.19, 200.190, 44.55]
        }

data = pd.DataFrame(table)
print(data)

# Delete existing Columns from DataFrame
del(data['basic'])
print('\n---After Deleting basic Column DataFrame---')
print(data)

x = data.pop('Age')
print('\n---After Deleting Age Column DataFrame---')
print(data)
print('\n---pop Column from DataFrame---')
print(x)

y = data.drop(columns = 'Sale')
print('\n---After Deleting Sale Column DataFrame---')
print(y)

OUTPUT

Delete DataFrame Row in Python

In this Python example, we are using the Pandas drop function to delete DataFrame row.

import pandas as pd

table = {'name': ['Kane', 'Suresh', 'Tracy'],
         'Profession': ['Manager', 'Developer', 'HR'],
         'Salary': [10000, 14000, 11000],
         'Sale': [422.19, 200.190, 44.55]
        }

data = pd.DataFrame(table, index = ['a', 'b', 'c'])
print(data)

x = data.drop('b')
print('\n---After Deleting b row DataFrame---')
print(x)

y = data.drop('a')
print('\n---After Deleting a row DataFrame---')
print(y)

OUTPUT

Rename Pandas DataFrame Column

In Python, you can use Pandas rename function to rename a column or multiple columns of a DataFrame. In this example, we use this Pandas DataFrame rename function to rename Profession column to Qualification and Salary to Income.

import pandas as pd

table = {'name': ['John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [25, 32, 30, 26],
         'Profession': ['Developer', 'Analyst', 'Admin', 'HR'],
         'Salary':[1000000, 1200000, 900000, 1100000]
         }
data = pd.DataFrame(table)
print(data)

# data = data.rename(columns = {'Profession': 'Qualification'})
data.rename(columns = {'Profession': 'Qualification'}, inplace = True)
print('\n---After Renaming Column in a DataFrame---')
print(data)

data.rename(columns =
                {'Profession': 'Qualification',
                'Salary': 'Income'},
            inplace = True)
print('\n---After Renaming two Column in a DataFrame---')
print(data)

OUTPUT

Python pandas head and tail

If you are coming from R programming, you might be familiar with head and tail functions. The Pandas DataFrame head function accepts integer value as an argument and returns Top or first given number of records. For example, head(5) returns Top 5 records from a dataFrame. Similarly, Python DataFrame tail function returns bottom or last records from a DataFrame. For example, tail(5) returns last 5 records or bottom 5 records from a DataFrame.

import pandas as pd
table = {'name': ['Kane', 'John', 'Mike', 'Suresh', 'Tracy', 'Steve'],
         'Age': [35, 25, 32, 30, 26, 29],
         'Profession': ['Manager', 'Developer', 'Analyst', 'Admin', 'HR', 'HOD'],
         'Sale':[422.19, 22.55, 12.66, 119.470, 200.190, 44.55],
         'Salary':[12000, 10000, 8000, 14000, 11000, 14000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---First Five records DataFrame head()---')
print(data.head())

print('\n---First two records DataFrame head(2)---')
print(data.head(2))

print('\n---Bottom Five records DataFrame tail()---')
print(data.tail())

print('\n---last two records DataFrame tail(2)---')
print(data.tail(2))

OUTPUT

Pandas Transpose DataFrame in Python

Python DataFrame has inbuilt functionality to transpose a Matrix. For this, you have to use DataFrame.T

import pandas as pd
table = {'name': ['Kane', 'John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [35, 25, 32, 30, 26],
         'Profession': ['Manager', 'Developer', 'Analyst', 'Admin', 'HR'],
         'Sale':[422.19, 22.55, 12.66, 119.470, 200.190],
         'Salary':[12000, 10000, 8000, 14000, 11000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---Transposed DataFrame---')
print(data.T)

OUTPUT

Python DataFrame groupby

A Python DataFrame groupby function is similar to Sql Server Group By clause. I mean, you can use this Pandas groupby function to group data by some columns and find the aggregated results of the other columns. This is one of the important concept or function, while working with real-time data. 

In this example, we created a DataFrame of different columns and data types. Next, we used this groupby function on that DataFrame. The first statement, data.groupby(‘Profession’).sum() groups DataFrame by Profession column and calculate the sum of Sales, Salary and Age. The second statement, data.groupby([‘Profession’, ‘Age’]).sum() groups DataFrame by Profession and Age columns and calculate the sum of Sales, and Salary. Remember, any string columns (unable to aggregate) will be concatenated or combined

import pandas as pd
table = {'name': ['Kane', 'Dave', 'Ram', 'John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [35, 25, 25, 35, 25, 35, 35],
         'Profession': ['Analyst', 'HR', 'Analyst', 'Admin', 'HR', 'Admin', 'HR'],
         'Sale':[422, 22, 55, 12, 119, 470, 200],
         'Salary':[12000, 9000, 10000, 8000, 14000, 20000, 11000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---DataFrame groupby Profession---')
print(data.groupby('Profession').sum())

print('\n---DataFrame groupby Profession and Age---')
print(data.groupby(['Profession', 'Age']).sum())

OUTPUT

Python DataFrame stack

A Python Pandas DataFrame stack function is used to compress one level of a DataFrame object. In order to use this DataFrame stack function, you can simply call data_to_stack.stack(). In this example, we are using this Python DataFrame stack function on grouped data (groupby function result) to further compress the DataFrame.

import pandas as pd
table = {'name': ['Kane', 'Dave', 'Ram', 'John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [35, 25, 25, 35, 25, 35, 35],
         'Profession': ['Analyst', 'HR', 'Analyst', 'Admin', 'HR', 'Admin', 'HR'],
         'Sale':[422, 22, 55, 12, 119, 470, 200],
         'Salary':[12000, 9000, 10000, 8000, 14000, 20000, 11000]
	    }
data = pd.DataFrame(table)

grouped_data1 = data.groupby('Profession').sum()
stacked_data1 = grouped_data1.stack()
print('\n---Stacked DataFrame groupby Profession---')
print(stacked_data1)

grouped_data2 = data.groupby(['Profession', 'Age']).sum()
stacked_data2 = grouped_data2.stack()
print('\n---Stacked DataFrame groupby Profession and Age---')
print(stacked_data2)

OUTPUT

Python DataFrame unstack

The Python Pandas DataFrame unstack function undo the operation done by stack function or say, opposite to stack function. This Python DataFrame unstack function uncompress the last column of a stacked DataFrame (.stack() function). In order to use this function, you can simply call stacked_data.unstack()

import pandas as pd
table = {'name': ['Kane', 'Dave', 'Ram', 'John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [35, 25, 25, 35, 25, 35, 35],
         'Profession': ['Analyst', 'HR', 'Analyst', 'Admin', 'HR', 'Admin', 'HR'],
         'Sale':[422, 22, 55, 12, 119, 470, 200],
         'Salary':[12000, 9000, 10000, 8000, 14000, 20000, 11000]
	    }
data = pd.DataFrame(table)

grouped_data1 = data.groupby('Profession').sum()
stacked_data1 = grouped_data1.stack()
unstacked_data1 = stacked_data1.unstack()
# print('\n---Stacked DataFrame groupby Profession---')
# print(stacked_data1)
print('\n---Unstacked DataFrame groupby Profession---')
print(unstacked_data1)

grouped_data2 = data.groupby(['Profession', 'Age']).sum()
stacked_data2 = grouped_data2.stack()
unstacked_data2 = stacked_data2.unstack()
# print('\n---Stacked DataFrame groupby Profession and Age---')
# print(stacked_data2)
print('\n---Unstacked DataFrame groupby Profession and Age---')
print(unstacked_data2)

OUTPUT

Python DataFrame Concatenation

A Pandas DataFrame concat function is used to combine or concatenate DataFrame objects. In this example, we declared two DataFrames of random values of a size 4 * 6. Next, we used concat function to concatenate DataFrames

import pandas as pd
import numpy as np

dataframe_one = pd.DataFrame(np.random.randn(4, 6))
print(dataframe_one)

dataframe_two = pd.DataFrame(np.random.randn(4, 6))
print(dataframe_two)

print('\n---DataFrame concatenation---')
print(pd.concat([dataframe_one, dataframe_two]))

OUTPUT

In the above example, we are concatenating two dataFrame objects of same size. However, you can use this Python Pandas DataFrame concat function to concatenate or combines more than two DataFrame Objects and different size. For this, we used three different size DataFrames of randomly generated numbers. Next, we used the Python DataFrame concat function to concat those three objects.

import numpy as np
import pandas as pd

dataframe_one = pd.DataFrame(np.random.randn(4, 6))
print(dataframe_one)

dataframe_two = pd.DataFrame(np.random.randn(4, 5))
print(dataframe_two)

dataframe_three = pd.DataFrame(np.random.randn(3, 4))
print(dataframe_three)

print('\n-----DataFrame concatenation-----')
print(pd.concat([dataframe_one, dataframe_two, dataframe_three]))

OUTPUT

Python Math operations on Pandas DataFrame

In this example, we are going to use few of the Python Pandas DataFrame mathematical functions. For the demonstration purpose, we are finding the Mean and Median of each column and each Row. To get the mean or median of each row, you have to place integer 1 inside the function.

import pandas as pd
table = {'name': ['Kane', 'John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [35, 25, 32, 30, 26],
         'Profession': ['Manager', 'Developer', 'Analyst', 'Admin', 'HR'],
         'Sale':[422.19, 22.55, 12.66, 119.470, 200.190],
         'Salary':[12000, 10000, 8000, 14000, 11000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---DataFrame Mean of Columns---')
print(data.mean())

print('\n---DataFrame Mean of Rows---')
print(data.mean(1))

print('\n---DataFrame Median of Columns---')
print(data.median())

print('\n---DataFrame Median of Rows---')
print(data.median(1))

OUTPUT

In this example, we are calculating the sum of all the rows of each column, sum of all columns in each row. Similarly, minimum value in a column, maximum value in each column, maximum value in each row using sum(), min() and max() functions.

import pandas as pd
table = {'name': ['Kane', 'John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [35, 25, 32, 30, 26],
         'Profession': ['Manager', 'Developer', 'Analyst', 'Admin', 'HR'],
         'Sale':[422.19, 22.55, 12.66, 119.470, 200.190],
         'Salary':[12000, 10000, 8000, 14000, 11000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---DataFrame sum of Columns---')
print(data.sum())

print('\n---DataFrame sum of Rows---')
print(data.sum(1))

print('\n---DataFrame Minimum of Columns---')
print(data.min())

print('\n---DataFrame Maximum of Columns---')
print(data.max())

print('\n---DataFrame Maximum of Rows---')
print(data.max(1))

OUTPUT

Arithmetic Operations on Python Pandas DataFrame

In this example, we are going to perform Arithmetic operations on Python DataFrame

import pandas as pd
table = {'Age': [25, 32, 30],
         'Sale':[422.19, 119.470, 200.190],
         'Salary':[12000, 14000, 11000]
	    }
data = pd.DataFrame(table)
print(data)

print('\n---Add 20 to DataFrame---')
print(data + 20)

print('\n---Subtract 10 from DataFrame---')
print(data - 10)

print('\n---Multiply DataFrame by 2---')
print(data * 2)

OUTPUT

Python Pandas DataFrame Nulls

The isnull check and returns True if a value in DataFrame is Null otherwise False. Python Pandas notnull function returns True if value is not Null otherwise, False is returned.

import pandas as pd
import numpy as np

table = {'name': ['Kane', 'Suresh', np.nan],
         'Profession': ['Manager', np.nan, 'HR'],
         'Salary': [np.nan, 14000, 11000],
         'Sale': [422.19, np.nan, 44.55]
        }

data = pd.DataFrame(table)
print(data)

print('\n---Checking Nulls in a DataFrame---')
print(data.isnull())

print('\n---Checking Not Nulls in a DataFrame---')
print(data.notnull())

OUTPUT

Pandas Replace Nulls in DataFrame

We can also replace those Null values with a meaningful numbers. For this, we can use Python DataFrame fillna function or replace function. Let me show you the implementation of both these functions.

import pandas as pd
import numpy as np

table = {'Age': [20, 35, np.nan],
         'Salary': [np.nan, 14000, 11000],
         'Sale': [422.19, np.nan, 44.55]
        }

data = pd.DataFrame(table)
print(data)

print('\n---Fill Missing Values DataFrame---')
print(data.fillna(30))

print('\n---Replace Missing Values DataFrame---')
print(data.replace({np.nan:66}))

OUTPUT

Pandas DataFrame pivot

Python Pandas DataFrame has a pivot function, which is very useful to pivot the existing DataFrame. Below example will show you the same

import pandas as pd

table = {'name': ['Kane', 'John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [35, 25, 32, 30, 26],
         'Profession': ['Manager', 'HR', 'Analyst', 'Manager', 'HR'],
         'Sale':[422.19, 22.55, 12.66, 119.470, 200.190],
         'Salary':[12000, 10000, 8000, 14000, 11000]
	    }
data = pd.DataFrame(table)
print(data)
print('\n--- After DataFrame Pivot---')
data2 = data.pivot(index = 'name', columns = 'Profession', values = 'Salary')
print(data2)

print('\n--- After DataFrame Pivot---')
data3 = data.pivot(index = 'name', columns = 'Profession')
print(data3)

OUTPUT

Pandas Save DataFrame to CSV and Text File

In this example, we will show you, how to load dat from a Pandas DataFrame to a csv file or text file. For this, you have to use the Pandas to_csv function.

import pandas as pd

table = {'name': ['Kane', 'John', 'Mike', 'Suresh', 'Tracy'],
         'Age': [35, 25, 32, 30, 26],
         'Profession': ['Manager', 'HR', 'Analyst', 'Manager', 'HR'],
         'Sale':[422.19, 22.55, 12.66, 119.470, 200.190],
         'Salary':[12000, 10000, 8000, 14000, 11000]
	    }
data = pd.DataFrame(table)
print(data)
# load DataFrame to text file
data.to_csv('user_info.txt') 
# load DataFrame to csv file with comma separator
data.to_csv('user_info.csv')
# load data from DataFrame to csv file with Tab separator
data.to_csv('user_info_new.csv', sep = '\t')

OUTPUT

Iterate over DataFrame Rows in Python

In Python, you can use any of the three functions iteritems, iterrows and itertuple to iterate over rows and returns each row of a DataFrame. 

import pandas as pd

table = {'name': ['Kane', 'John', 'Mike'],
         'Age': [35, 25, 32],
         'Profession': ['Manager', 'HR', 'Analyst'],
         'Sale':[422.19, 119.470, 200.190],
         'Salary':[12000, 14000, 11000]
	    }
data = pd.DataFrame(table)
print(data)
print('\n---Iterating Rows---')
for rows, columns in data.iterrows():
    print(rows, columns)
    print()

OUTPUT