The nlargest
method in Pandas is used to return the largest n
elements from a DataFrame or Series.
This method takes the number n
as an argument, and returns a new DataFrame or Series containing the n
largest elements from the original DataFrame or Series.
For example, consider the following DataFrame:
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
This DataFrame has three columns A
, B
, and C
, with five rows of data.
To get the largest three elements from the B
column, you could do the following:
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
# Get the largest three elements from column B
largest = df['B'].nlargest(3)
# Print the resulting Series
print(largest)
4 50
3 40
2 30
Name: B, dtype: int64
In the code above, the nlargest
method is applied to the B
column of the DataFrame, and the number 3
is passed as an argument.
This returns a new Series
object containing the three largest elements from the original B
column.
In this case, the resulting Series
has the values 50
, 40
, 30
.
You can also use the nlargest
method to get the largest n
elements from a DataFrame by specifying the column
argument.
For example, if you wanted to get the largest three elements from the entire DataFrame, you could do the following:
import pandas as pd
df = pd.DataFrame({
'A': [1, 2, 3, 4, 5],
'B': [10, 20, 30, 40, 50],
'C': [100, 200, 300, 400, 500]
})
# Get the largest three elements from the DataFrame
largest = df.nlargest(3, columns='B')
# Print the resulting DataFrame
print(largest)
In the code above, the nlargest
method is applied to the DataFrame, and the columns
argument is used to specify that the B
column should be used to determine the largest elements.
The number 3
is also passed as an argument, which specifies that the largest three elements should be returned.
The nlargest
method returns a new DataFrame containing the three largest elements from the original DataFrame, based on the values in the B
column.
In this case, the resulting DataFrame has three rows with the values:
A B C
4 5 50 500
3 4 40 400
2 3 30 300
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