Get List From Pandas DataFrame Series

Get List From Pandas DataFrame Series

Python is a well-known language for data analysis, mainly due to the Python packages. Pandas is one of those packages that help us analyze data much easier.

Pandas tolist() method converts a series into a series or built-in list of Python. By default, the series is the type of pandas.core.series.Series data type and tolist() method, converted to a list of data.

Use the tolist() Method to Get List From Pandas DataFrame Series

This article will discuss how to get a list from Pandas Dataframe column. We will first read a CSV file into a Pandas DataFrame.

import pandas as pd
# read csv file
df=pd.read_csv('home_price.csv')
# display 3 rows 
df=df.head(3)
print(df)

Output:

   Area  Home price
0  1000       10000
1  1200       12000
2  1300       13000

Now we will extract the value from the column and convert it to the list as we know that tolist() helps.

list1=df['Home price'].values.tolist()
print('extract the value of series and converting into the list')
print(list1)

Output:

extract the value of series and converting into the list
[10000, 12000, 13000, 14000, 15000]

The list is an ordered and flexible Python container, one of the most common data structures in Python. Elements are inserted into square brackets [], separated by commas to create a list. The list can contain duplicate values; that’s why we mainly use lists in datasets.

import numpy as np
import pandas as pd
# read csv file
df=pd.read_csv('home_price.csv')
# extract the value of series and converting into the list
list1=df['Home price'].values.tolist()
list1=np.array(list1)
# type casting in list data type
updated=list(list1*1.5)
print('after include 1.5 % tax\n')
print(updated,'new home price')
df['Home price']=updated
# create new csv
df.to_csv('home prices after 1 year.csv')
df2= pd.read_csv('home prices after 1 year.csv')
print(df2)

In this case, prices are increased by 1.5 tax in present days. Now we create a list named updated list and update the existing column; further, we create a new CSV file using the to_csv() method.

Output:

after include 1.5 % tax

[15000.0, 18000.0, 19500.0, 21000.0, 22500.0] new home price
   Unnamed: 0  Area  Home price
0           0  1000     15000.0
1           1  1200     18000.0
2           2  1300     19500.0
3           3  1400     21000.0
4           4  1500     22500.0

Let’s consider another simple example:

import pandas as pd
df=pd.DataFrame({'Country':['Pakistan','India','America','Russia','China'],
                 'Immigrants':['2000','2500','6000','4000','1000'],
                 'Years':['2010','2008','2011','2018','2016']})
print(df,'\n')
list=df.columns.tolist()
print(type(df.columns))
print('\n',list,'\n')
print('After type cast into the list')
print(type(list))

Please observe that the series data type is changed by tolist(), and we got a list with all columns of Dataframe.

Output:

    Country Immigrants Years
0  Pakistan       2000  2010
1     India       2500  2008
2   America       6000  2011
3    Russia       4000  2018
4     China       1000  2016 

<class 'pandas.core.indexes.base.Index'>

 ['Country', 'Immigrants', 'Years'] 

After type cast into the list
<class 'list'>

All the codes are in here.

import numpy as np
import pandas as pd
# read csv file
df=pd.read_csv('home_price.csv')
# display 3 rows
df=df.head(3)
print(df)

list1=df['Home price'].values.tolist()
print('extract the value of series and converting into the list')
print(list1)

# another example
# read csv file
df=pd.read_csv('home_price.csv')
# extract the value of series and converting into the list
list1=df['Home price'].values.tolist()
list1=np.array(list1)
# type casting in list data type
updated=list(list1*1.5)
print('after include 1.5 % tax\n')
print(updated,'new home price')
df['Home price']=updated
# create new csv
df.to_csv('home prices after 1 year.csv')
df2= pd.read_csv('home prices after 1 year.csv')
print(df2)

# another example
df=pd.DataFrame({'Country':['Pakistan','India','America','Russia','China'],
                 'Immigrants':['2000','2500','6000','4000','1000'],
                 'Years':['2010','2008','2011','2018','2016']})
print(df,'\n')
list=df.columns.tolist()
print(type(df.columns))
print('\n',list,'\n')
print('After type cast into the list')
print(type(list))

Output:

   Area  Home price
0  1000       10000
1  1200       12000
2  1300       13000
extract the value of series and converting into the list
[10000, 12000, 13000]
after include 1.5 % tax

[15000.0, 18000.0, 19500.0, 21000.0, 22500.0] new home price
   Unnamed: 0  Area  Home price
0           0  1000     15000.0
1           1  1200     18000.0
2           2  1300     19500.0
3           3  1400     21000.0
4           4  1500     22500.0
    Country Immigrants Years
0  Pakistan       2000  2010
1     India       2500  2008
2   America       6000  2011
3    Russia       4000  2018
4     China       1000  2016 

<class 'pandas.core.indexes.base.Index'>

 ['Country', 'Immigrants', 'Years'] 

After type cast into the list
<class 'list'>

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