cummax function in pandas | cummax pandas example
In this pandas tutorial, we will discuss about:
-
cummax function in pandas,
-
cummax pandas example
Before we start applying cummax function in pandas on the pandas DataFrame lets create one dataframe first.
DataFrame in pandas is an two dimensional data structure that will store data in two dimensional format. One dimension refers to a row and second dimension refers to a column, So It will store the data in rows and columns.
We can able to create this DataFrame using DataFrame() method. But this is available in pandas module, so we have to import pandas module.
Syntax:
pandas.DataFrame(data)
Where, data is the input dataframe, The data can be a dictionary that stores list of values with specified key.
Example: Create DataFrame
In this example, we will create a dataframe with 4 rows and 3 columns with building data and assign indices through index parameter.
import pandas as pd
#create dataframe from the building data
data= pd.DataFrame({
'length':[5.6,7.8,4.5,5.3],
"breadth":[12.9,4.5,21.5,6.0],
"area":[20,56,43,45]
},index=['one','two','three','four'])
#display the dataframe
print(data)
Output: DataFrame is created below
length breadth area
one 5.6 12.9 20
two 7.8 4.5 56
three 4.5 21.5 43
four 5.3 6.0 45
Now lets see few cummax pandas example.
cummax function in pandas
cummax function in pandas or cummax() will return the cumulative maximum of values for the given dataframe.
Syntax:
dataframe.cummax(axis,skipna)
This will return the entire dataframe
Parameters:
-
axis=0 specifies row and axis= 1 specifies column to get cumulative maximum along row/column
-
skipna will take boolean values - True or False. If False,It will consider NaN values and If True,It will not consider NaN values in cumulative maximum operation.
If we want to get the cumulative maximum of values in a column for the given dataframe, we have to specify the column.
Syntax:
dataframe['column'].cummax()
where, dataframe is the input dataframe and column is the column name.
This will return the specified column cumulative maximum in the given dataframe.
Lets see few pandas cummax example.
Example 1: cummax pandas example
Here in pandas cummax example, we will get the cumulative maximum for the entire dataframe and in a specific column.
import pandas as pd
#create dataframe from the college data
data= pd.DataFrame({
'length':[5.6,7.8,4.5,5.3],
"breadth":[12.9,4.5,21.5,6.0],
"area":[20,56,43,45]
},index=['one','two','three','four'])
# get the cumulative maximum
print(data.cummax())
print()
# get the cumulative maximum from length column
print(data['length'].cummax())
print()
# get the cumulative maximum from area column
print(data['area'].cummax())
Output: pandas cummax example result
length breadth area
one 5.6 12.9 20
two 7.8 12.9 56
three 7.8 21.5 56
four 7.8 21.5 56
one 5.6
two 7.8
three 7.8
four 7.8
Name: length, dtype: float64
one 20
two 56
three 56
four 56
Name: area, dtype: int64
Lets see one more pandas cummax example with skipna.
Example 2: cummax pandas example
Here in cummax pandas example we will be dealing with skipna parameter.
import pandas as pd
import numpy as np
#create dataframe from the college data
data= pd.DataFrame({
'length':[np.nan,7.8,4.5,np.nan],
"breadth":[12.9,4.5,21.5,np.nan],
"area":[2,np.nan,56,43]
},index=['one','two','three','four'])
# get the cumulative maximum with out nan values
print(data.cummax(skipna=True))
print()
# get the cumulative maximum by considering nan values
print(data.cummax(skipna=False))
Output: cummax pandas example result
length breadth area
one NaN 12.9 2.0
two 7.8 12.9 NaN
three 7.8 21.5 56.0
four NaN NaN 56.0
length breadth area
one NaN 12.9 2.0
two NaN 12.9 NaN
three NaN 21.5 NaN
four NaN NaN NaN
This wraps our session on how to use cummax function in pandas and cummax pandas example.
Would you like to see your article here on tutorialsinhand.
Join
Write4Us program by tutorialsinhand.com
About the Author
Gottumukkala Sravan Kumar 171FA07058
B.Tech (Hon's) - IT from Vignan's University.
Published 1400+ Technical Articles on Python, R, Swift, Java, C#, LISP, PHP - MySQL and Machine Learning
Page Views :
Published Date :
Apr 08,2022