Use shape[]
, len()
, list()
and info()
function to count columns from pandas DataFrame. In this article, I will explain how to count the number of columns from DataFrame by using these functions with examples.
1. Quick Examples of Count Columns of DataFrame
If you are in a hurry, below are some quick examples of how to count columns in DataFrame.
# Below are some quick examples
# Example 1:Pandas count columns
# Using DataFrame.shape()
df2 = df.shape[1]
# Example 2: Pandas count columns and rows
df2 = df.shape
# Example 3: Pandas count columns using len()
df2 = len(df.columns)
# Example 4: Using columns property
col = df.columns
df2 = len(col)
# Example 5: Pandas count columns using list()
df_list = list(df)
df2 = len(df_list)
# Example 5: Using DataFrame.info() function
df2 = df.info()
Now, Let’s create Pandas DataFrame using data from a Python dictionary, where the columns are Courses
, Fee
, Duration
and Discount
.
# Create Count Columns of DataFrame
import pandas as pd
import numpy as np
technologies= ({
'Courses':["Spark","PySpark","Hadoop","Pandas"],
'Fee': [22000,25000,30000,35000],
'Duration':['30days','50days','40days','35days'],
'Discount':[1000,2000,2500,1500]
})
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)
Yields below output.
# Output:
Courses Fee Duration Discount
r1 Spark 22000 30days 1000
r2 PySpark 25000 50days 2000
r3 Hadoop 30000 40days 2500
r4 Pandas 35000 35days 1500
2. Use DataFrame.shape() Function to count columns
Pandas DataFrame provides a shape property that returns the number of count columns and rows shape of the DataFrame in a tuple, where the shape[0]
element is a row count and shape[1]
is the columns count. Below is an example. To learn more about shape, refer to DataFrame.shape[]
# Pandas count columns using DataFrame.shape()
df2 = df.shape[1]
print(df2)
# Output:
# 4
# Pandas count columns and rows
df2 = df.shape
print(df2)
# Output:
# (4, 4)
3. Use len() Function to Count Columns
You can also use len()
function to count columns in a DataFrame. For example len(df.columns)
returns the number of columns in a DataFrame.
# Pandas count columns using len()
df2 = len(df.columns)
print(df2)
# Using columns property
col = df.columns
df2 = len(col)
print(df2)
# Output:
# 4
4. Use list() to count columns
Alternatively, You can also use the list()
with the combination of len() function to get the count of DataFrame columns. Here, list() takes the dataframe as input and returns the data in a list.
# Pandas count columns using list()
df_list = list(df)
df2 = len(df_list)
print(df2)
# Output:
# 4
5. Using Pandas DataFrame.info() Function
Pandas DataFrame.info()
function provides information about the DataFrame including dtype of columns, index, memory usage, count columns, etc.
# Using DataFrame.info() function
df2 = df.info()
print(df2)
Yields below output.
# Output:
Index: 4 entries, r1 to r4
Data columns (total 4 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Courses 4 non-null object
1 Fee 4 non-null int64
2 Duration 4 non-null object
3 Discount 4 non-null int64
dtypes: int64(2), object(2)
memory usage: 160.0+ bytes
None
6. Complete Example For Count Columns
import pandas as pd
import numpy as np
technologies= ({
'Courses':["Spark","PySpark","Hadoop","Pandas"],
'Fee': [22000,25000,30000,35000],
'Duration':['30days','50days','40days','35days'],
'Discount':[1000,2000,2500,1500]
})
index_labels=['r1','r2','r3','r4']
df = pd.DataFrame(technologies,index=index_labels)
print(df)
# Pandas count columns using DataFrame.shape()
df2 = df.shape[1]
print(df2)
# Pandas count columns and rows
df2 = df.shape
print(df2)
# Pandas count columns using len()
df2 = len(df.columns)
print(df2)
# Using columns property
col = df.columns
df2 = len(col)
print(df2)
# Pandas count columns using list()
df_list = list(df)
df2 = len(df_list)
print(df2)
# Using DataFrame.info() function
df2 = df.info()
print(df2)
7. Conclusion
In this article, I have explained how to count columns in pandas DataFrame using shape[]
, len()
, list()
, and info()
functions with examples.
Happy Learning !!
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