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In Pandas DataFrame, data is stored or displayed in tabular formats like rowsand . Pandas helps us to retrieve or count the number of rows and columns in columnsby using various methods .DataFrame

DataFrameWe will explore various methods related to counting the number of columns in Pandas in this tutorial .

columnCounting DataFramethe number of columns in Pandas using the attribute

Using Pandas DataFrame's columnattribute, we can retrieve the list of columns and calculate the column lengths and count DataFramethe number of columns in .

See the following example. First, we create a product of DataFrame. Using column_list = dataframe.columns, we retrieve the list of columns and then use len(column_list)to count the number of columns.

Sample code:

import pandas as pd
import numpy as np
from IPython.display import display

# creating a DataFrame
dict = {
    "Products": ["Intel Dell Laptops", "HP Laptops", "Lenavo Laptops", "Acer Laptops"],
    "Price dollar": [350, 300, 400, 250],
    "Percentage Sale": [83, 99, 84, 76],
}
dataframe = pd.DataFrame(dict)

# displaying the DataFrame
display(dataframe)

# To get the list of columns of dataframe
column_list = dataframe.columns

# Printing Number of columns
print("Number of columns:", len(column_list))

Output:

shapeCounting DataFramethe number of columns in Pandas using the attribute

When the attribute is used shape, it retrieves DataFramea tuple representing the shape of . In the following example, shape=dataframe.shapethe rows are returned DataFramein shape, while shape[1]counts the number of columns.

Sample code:

import pandas as pd
import numpy as np
from IPython.display import display

# creating a DataFrame
dict = {
    "Products": ["Intel Dell Laptops", "HP Laptops", "Lenavo Laptops", "Acer Laptops"],
    "Price dollar": [350, 300, 400, 250],
    "Percentage Sale": [83, 99, 84, 76],
    "quantity": [10, 16, 90, 100],
}
dataframe = pd.DataFrame(dict)

# displaying the DataFrame
display(dataframe)

# Get shape of the dataframe
shape = dataframe.shape

# Printing Number of columns
print("Number of columns :", shape[1])

Output:

As we can see in the above output, it shows 4the total of for the above example 列数.

DataFrameCounting the number of columns in Pandas using type conversion

We have used the type conversion method in this method which is almost like the column attribute. When we DataFrameuse with a list typecasting, it retrieves a list of column names. For more understanding of the type conversion method, see the following example:

Sample code:

import pandas as pd
import numpy as np
from IPython.display import display

# creating a DataFrame
dict = {
    "Products": ["Intel Dell Laptops", "HP Laptops", "Lenavo Laptops", "Acer Laptops"],
    "Price dollar": [350, 300, 400, 250],
    "Percentage Sale": [83, 99, 84, 76],
    "quantity": [10, 16, 90, 100],
}
dataframe = pd.DataFrame(dict)

# displaying the DataFrame
display(dataframe)

# Typecasting dataframe to list
dataframe_list = list(dataframe)

# Printing Number of columns
print("Number of columns :", len(dataframe_list))

Output:

dataframe.info()Count DataFramethe number of columns in Pandas using the

Using info()the pandas.c method, we can print DataFramea complete concise summary of Pandas. In the following example, we have used it at the end of the source code dataframe.info(). It displays information related to DataFramethe class, dtypes, memory usage, number of columns, and range index.

Sample code:

import pandas as pd
import numpy as np
from IPython.display import display

# creating a DataFrame
dict = {
    "Products": ["Intel Dell Laptops", "HP Laptops", "Lenavo Laptops", "Acer Laptops"],
    "Price dollar": [350, 300, 400, 250],
    "Percentage Sale": [83, 99, 84, 76],
    "quantity": [10, 16, 90, 100],
}
dataframe = pd.DataFrame(dict)

# displaying the DataFrame
display(dataframe)

# Print dataframe information using info() method
dataframe.info()

Output:

In the image above, we can see DataFramea concise summary of , including the number of columns.