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'>