Different Ways of Creating DataFrame With Python

A Data Frame is a two-dimension collection of data. It is a data structure where data is stored in tabular form. Datasets are arranged in rows and columns; we can store multiple datasets in the data frame. We can perform various arithmetic operations, such as adding column/row selection and columns/rows in the data frame.

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We can import the DataFrames from the external storage; these storages can be referred to as the SQL.  A data frame consists of index, column names and the data itself. The index is like a label for each row and together with the column names acts as an address to each data element. The data in a DataFrame can be heterogeneous, i.e., they can be of different data types.

Database, CSV file, and an Excel file. We can also use the lists, dictionary, and from a list of dictionary, etc.

Different methods of Creating DataFrame

An empty dataframe

We can create a basic empty Dataframe. The dataframe constructor needs to be called to create the DataFrame. Let’s understand the following example.

Example –

# import pandas as pd

import pandas as pd

# Calling DataFrame constructor

df = pd.DataFrame()

print(df)

Output:

Empty DataFrame

Columns: []

Index: []

Create a dataframe using List

We can create dataframe using a single list or list of lists. Let’s understand the following example.

Example –

 

# importing pandas library

import pandas as pd

 

# string values in the list

lst = [‘Java’, ‘Python’, ‘C’, ‘C++’,

‘JavaScript’, ‘Swift’, ‘Go’]

 

# Calling DataFrame constructor on list

dframe = pd.DataFrame(lst)

print(dframe)

Output:

0        Java1      Python2           C3         C++4   JavaScript5       Swift6          Go

Create Dataframe from dict of ndarray/lists

The dict of ndarray/lists can be used to create a dataframe, all the ndarray must be of the same length. The index will be a range(n) by default; where n denotes the array length. Let’s understand the following example.

Example

import pandas as pd

 

# assign data of lists.

data = {‘Name’: [‘Tom’, ‘Joseph’, ‘Krish’, ‘John’], ‘Age’: [20, 21, 19, 18]}

 

# Create DataFrame

df = pd.DataFrame(data)

 

# Print the output.

print(df)

Output:

Name  Age0     Tom   201  Joseph   212   Krish   193    John   18

Create a indexes Dataframe using arrays

Let’s understand the following example to create the indexes dataframe using arrays.

Example –

# DataFrame using arrays.

import pandas as pd

 

# assign data of lists.

data = {‘Name’:[‘Renault’, ‘Duster’, ‘Maruti’, ‘Honda City’], ‘Ratings’:[9.0, 8.0, 5.0, 3.0]}

 

# Creates pandas DataFrame.

df = pd.DataFrame(data, index =[‘position1’, ‘position2’, ‘position3’, ‘position4’])

 

# print the data

print(df)

Output:

Name      Ratingsposition1     Renault      9.0position2      Duster      8.0position3      Maruti      5.0position4    Honda City      3.0

In the above code, we have defined the column name with the various car names and their ratings. We used the array to create indexes.

Create Dataframe from list of dicts

We can pass the lists of dictionaries as input data to create the Pandas dataframe. The column names are taken as keys by default. Let’s understand the following example.

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Example –

# the example is to create

# Pandas DataFrame by lists of dicts.

import pandas as pd

 

# assign values to lists.

data = [{‘A’: 10, ‘B’: 20, ‘C’:30}, {‘x’:100, ‘y’: 200, ‘z’: 300}]

 

# Creates DataFrame.

df = pd.DataFrame(data)

 

# Print the data

print(df)

Output:

A      B      C      x      y      z0  10.0  20.0  30.0    NaN    NaN    NaN1   NaN   NaN   NaN  100.0  200.0  300.0

Create Dataframe using the zip() function

The zip() function is used to merge the two lists. Let’s understand the following example.

Example –

# The example is to create

# pandas dataframe from lists using zip.

 

import pandas as pd

 

# List1

Name = [‘tom’, ‘krish’, ‘arun’, ‘juli’]

 

# List2

Marks = [95, 63, 54, 47]

 

#  two lists.

# and merge them by using zip().

list_tuples = list(zip(Name, Marks))

 

# Assign data to tuples.

print(list_tuples)

 

# Converting lists of tuples into

# pandas Dataframe.

dframe = pd.DataFrame(list_tuples, columns=[‘Name’, ‘Marks’])

 

# Print data.

print(dframe)

Output:

[(‘john’, 95), (‘krish’, 63), (‘arun’, 54), (‘juli’, 47)]    Name  Marks0   john     951  krish     632   arun     543   juli     47

Create Dataframe from Dicts of series

The dictionary can be passed to create a dataframe. We can use the Dicts of series where the subsequent index is the union of all the series of passed index value. Let’s understand the following example.

Example –

# Pandas Dataframe from Dicts of series.

 

import pandas as pd

 

# Initialize data to Dicts of series.

d = {‘Electronics’ : pd.Series([97, 56, 87, 45], index =[‘John’, ‘Abhinay’, ‘Peter’, ‘Andrew’]),

‘Civil’ : pd.Series([97, 88, 44, 96], index =[‘John’, ‘Abhinay’, ‘Peter’, ‘Andrew’])}

 

# creates Dataframe.

dframe = pd.DataFrame(d)

 

# print the data.

print(dframe)

Output:

Electronics      CivilJohn             97        97Abhinay      56        88Peter           87        44Andrew      45        96

How to create new variables in the data frame

Creating a new variable in pandas data frame is an easy task! Either you can pass the values of that new column or you can generate the values of new columns based on the existing columns.

The code snippet shown below creates two new columns based on the Age column.

# Defining Employee Data import pandas as pd import numpy as np EmployeeData=pd.DataFrame({‘Name’: [‘ram’,’ravi’,’sham’,’sita’,’gita’], ‘id’: [101,102,103,104,105], ‘Gender’: [‘M’,’M’,’M’,’F’,’F’], ‘Age’: [21,25,24,28,25] }) # Priting data print(EmployeeData)   # Creating a new variable in data based on existing variable EmployeeData[‘NewAge’]= EmployeeData[‘Age’] + 10   # Priting data print(EmployeeData)     # Creating a new variable in data based on existing variable EmployeeData[‘AgeSquared’]= EmployeeData[‘Age’] **2   # Priting data print(EmployeeData)   # Creating a new variable in data based on existing variable EmployeeData[‘AgeLOG’]= np.log(EmployeeData[‘Age’])   # Priting data print(EmployeeData)

Sometimes the logic to be applied for each value of an existing column may be a little complex to fit in one line, so we define a function for that and apply that function to each value of the column. The results are stored as a new column.

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