Different ways to create Pandas Dataframe
Pandas DataFrame is a 2-dimensional labeled data structure like a table with rows and columns. The size and values of the DataFrame are mutable, i.e., can be modified.
DataFrame is mostly used in data analysis and data manipulation. It lets you store data in tabular form like SQL database, MS Excel, or Google Sheets, making it easier to perform arithmetic operations on the data.
It is the most commonly used Pandas object. The DataFrame() function is used to create a DataFrame in Pandas. You can also create Pandas DataFrame in multiple ways.
Pandas Dataframe() Syntax
pandas.DataFrame(data, index, columns)
Parameters:
- data: It is a dataset from which a DataFrame is to be created. It can be a list, dictionary, scalar value, series, and arrays, etc.
- index: It is optional, by default the index of the DataFrame starts from 0 and ends at the last data value(n-1). It defines the row label explicitly.
- columns: This parameter is used to provide column names in the DataFrame. If the column name is not defined by default, it will take a value from 0 to n-1.
Returns:
Now that we have discussed about DataFrame() function, let’s look at different ways to create a DataFrame:
Different Ways to Create Dataframe in Python
There are several ways to create a Pandas Dataframe in Python. You can create a DataFrame with the following methods:
- Create Pandas DataFrame using DataFrame() function
- Create Pandas DataFrame from list of lists
- Create Pandas DataFrame from the dictionary of ndarray/list
- Create Pandas DataFrame from list of dictionaries
- Create Pandas DataFrame from a dictionary of Series
- Creating DataFrame using the zip() function
- Creating a DataFrame by proving the index label explicitly
Create an Empty DataFrame using DataFrame() Method
DataFrame in Python can be created by the DataFrame() function of the Pandas library. Just call the function with the DataFrame constructor to create a DataFrame.
Example: Creating an empty DataFrame using the DataFrame() function in Python
Python3
import pandas as pd
df = pd.DataFrame()
print (df)
|
Output:
Empty DataFrame
Columns: []
Index: []
Create DataFrame from lists of lists
To create a Pandas DataFrame from a list of lists, you can use the pd.DataFrame() function. This function takes a list of lists as input and creates a DataFrame with the same number of rows and columns as the input list.
Example: Creating DataFrame from lists of lists using the DataFrame() method
Python3
import pandas as pd
data = [[ 'tom' , 10 ], [ 'nick' , 15 ], [ 'juli' , 14 ]]
df = pd.DataFrame(data, columns = [ 'Name' , 'Age' ])
print (df)
|
Output:
Name Age
0 tom 10
1 nick 15
2 juli 14
Create DataFrame from Dictionary of ndArray/Lists
To create DataFrame from a dictionary of ndarrays/lists, all the arrays must be of the same length. If an index is passed then the length index should be equal to the length of the arrays.
If no index is passed, then by default, the index will be range(n) where n is the array length.
Example: Creating DataFrame from a dictionary of ndarray/lists
Python3
import pandas as pd
data = { 'Name' : [ 'Tom' , 'nick' , 'krish' , 'jack' ],
'Age' : [ 20 , 21 , 19 , 18 ]}
df = pd.DataFrame(data)
print (df)
|
Output:
Name Age
0 Tom 20
1 nick 21
2 krish 19
3 jack 18
Note: While creating DataFrame using a dictionary, the keys of the dictionary will be column names by default. We can also provide column names explicitly using column parameter.
Create DataFrame from List of Dictionaries
Pandas DataFrame can be created by passing lists of dictionaries as input data. By default, dictionary keys will be taken as columns.
Python3
import pandas as pd
data = [{ 'a' : 1 , 'b' : 2 , 'c' : 3 },
{ 'a' : 10 , 'b' : 20 , 'c' : 30 }]
df = pd.DataFrame(data)
print (df)
|
Output:
a b c
0 1 2 3
1 10 20 30
Another example is to create a Pandas DataFrame by passing lists of dictionaries and row indexes.
Python3
import pandas as pd
data = [{ 'b' : 2 , 'c' : 3 }, { 'a' : 10 , 'b' : 20 , 'c' : 30 }]
df = pd.DataFrame(data, index = [ 'first' , 'second' ])
print (df)
|
Output:
b c a
first 2 3 NaN
second 20 30 10.0
Create DataFrame from a dictionary of Series
To create a DataFrame from a dictionary of series, a dictionary can be passed to form a DataFrame. The resultant index is the union of all the series of passed indexed.
Example: Creating a DataFrame from a dictionary of series.
Python3
import pandas as pd
d = { 'one' : pd.Series([ 10 , 20 , 30 , 40 ],
index = [ 'a' , 'b' , 'c' , 'd' ]),
'two' : pd.Series([ 10 , 20 , 30 , 40 ],
index = [ 'a' , 'b' , 'c' , 'd' ])}
df = pd.DataFrame(d)
print (df)
|
Output:
one two
a 10 10
b 20 20
c 30 30
d 40 40
Create DataFrame using the zip() function
Two lists can be merged by using the zip() function. Now, create the Pandas DataFrame by calling pd.DataFrame() function.
Example: Creating DataFrame using zip() function.
Python3
import pandas as pd
Name = [ 'tom' , 'krish' , 'nick' , 'juli' ]
Age = [ 25 , 30 , 26 , 22 ]
list_of_tuples = list ( zip (Name, Age))
list_of_tuples
df = pd.DataFrame(list_of_tuples,
columns = [ 'Name' , 'Age' ])
print (df)
|
Output:
Name Age
0 tom 25
1 krish 30
2 nick 26
3 juli 22
Create a DataFrame by proving the index label explicitly
To create a DataFrame by providing the index label explicitly, you can use the index parameter of the pd.DataFrame() constructor. The index parameter takes a list of index labels as input, and the DataFrame will use these labels for the rows of the DataFrame.
Example: Creating a DataFrame by proving the index label explicitly
Python3
import pandas as pd
data = { 'Name' : [ 'Tom' , 'Jack' , 'nick' , 'juli' ],
'marks' : [ 99 , 98 , 95 , 90 ]}
df = pd.DataFrame(data, index = [ 'rank1' ,
'rank2' ,
'rank3' ,
'rank4' ])
print (df)
|
Output:
Name marks
rank1 Tom 99
rank2 Jack 98
rank3 nick 95
rank4 juli 90
Conclusion
Python Pandas DataFrame is similar to a table with rows and columns. It is a two-dimensional data structure and is very useful for data analysis and data manipulation.
In this tutorial, we have discussed multiple ways of creating a Pandas DataFrame. With this tutorial, you will be able to handle any complex requirement of creating DataFrame.
Last Updated :
18 Jan, 2024
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