Get List From Pandas DataFrame Series - Delft Stack

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How to 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'> Enjoying our tutorials? Subscribe to DelftStack on YouTube to support us in creating more high-quality video guides. Subscribe

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