Hello, readers! In this article, we will be focusing on different ways to print column names in Python. So, let us get started! We often come across questions and problem statements wherein we feel the need to deal with data in an excel or
csv file i.e. in the form of rows and columns. Python, as a programming language, provides us with a data structure called ‘DataFrame’ to deal with rows and columns. A Python DataFrame consists of rows and columns and the Pandas module offers us various functions to manipulate and deal with the data occupied within these rows and columns. Today, we will be having a look at the various different ways through which we can fetch and display the column header/names of a dataframe or a csv file. We would be referring the below csv
file in the below examples–First, where do you find columns in Python?
1. Using pandas.dataframe.columns to print column names in Python
We can use pandas.dataframe.columns
variable to print the column tags or headers at ease. Have a look at the below syntax!
Example:
import pandas file = pandas.read_csv["D:/Edwisor_Project - Loan_Defaulter/bank-loan.csv"] for col in file.columns: print[col]
In this example, we have loaded the csv file into the environment. Further, we have printed the column names through a for loop using dataframe.columns variable.
Output:
age ed employ address income debtinc creddebt othdebt default
2. Using pandas.dataframe.columns.values
Python provides us with pandas.dataframe.columns.values
to extract the column names from the dataframe or csv file and print them.
Syntax:
Example:
import pandas file = pandas.read_csv["D:/Edwisor_Project - Loan_Defaulter/bank-loan.csv"] print[file.columns.values]
So, the data.columns.values gives us a list of column names/headers present in the dataframe.
Output:
['age' 'ed' 'employ' 'address' 'income' 'debtinc' 'creddebt' 'othdebt' 'default']
3. Python sorted[] method to get the column names
Python sorted[]
method can be used to get the list of column names of a dataframe in an
ascending order of columns.
Have a look at the below syntax!
Syntax:
Example:
import pandas file = pandas.read_csv["D:/Edwisor_Project - Loan_Defaulter/bank-loan.csv"] print[sorted[file]]
Output:
['address', 'age', 'creddebt', 'debtinc', 'default', 'ed', 'employ', 'income', 'othdebt']
Conclusion
By this, we have come to the end of this topic. Hope this article turns out to be a hack for you in terms of different solutions for a single problem statement.
For more such posts related to Python, Stay tuned and till then, Happy Learning!! 🙂
A Data frame is a two-dimensional data structure, i.e., data is aligned in a tabular fashion in rows and columns. We can perform basic operations on rows/columns like selecting, deleting, adding, and renaming. In this article, we are using nba.csv
file.
Dealing with Columns
In order to deal with columns, we perform basic operations on columns like selecting, deleting, adding and renaming.
Column Selection:
In Order to select a column in Pandas DataFrame, we can either access the
columns by calling them by their columns name.
import
pandas as pd
data
=
{
'Name'
:[
'Jai'
,
'Princi'
,
'Gaurav'
,
'Anuj'
],
'Age'
:[
27
,
24
,
22
,
32
],
'Address'
:[
'Delhi'
,
'Kanpur'
,
'Allahabad'
,
'Kannauj'
],
'Qualification'
:[
'Msc'
,
'MA'
,
'MCA'
,
'Phd'
]}
df
=
pd.DataFrame[data]
print
[df[[
'Name'
,
'Qualification'
]]]
Output:
For more examples refer to How to select multiple columns in a pandas dataframe
Column Addition:
In Order to add a column in Pandas DataFrame, we can declare a new list as a column and add to a existing Dataframe.
import
pandas as pd
data
=
{
'Name'
: [
'Jai'
,
'Princi'
,
'Gaurav'
,
'Anuj'
],
'Height'
: [
5.1
,
6.2
,
5.1
,
5.2
],
'Qualification'
: [
'Msc'
,
'MA'
,
'Msc'
,
'Msc'
]}
df
=
pd.DataFrame[data]
address
=
[
'Delhi'
,
'Bangalore'
,
'Chennai'
,
'Patna'
]
df[
'Address'
]
=
address
print
[df]
Output:
For more examples refer to Adding new column to existing DataFrame in Pandas
Column Deletion:
In Order to delete a column in Pandas DataFrame, we can use the
drop[]
method. Columns is deleted by dropping columns with column names.import
pandas as pd
data
=
pd.read_csv[
"nba.csv"
, index_col
=
"Name"
]
data.drop[[
"Team"
,
"Weight"
], axis
=
1
, inplace
=
True
]
print
[data]
Output:
As shown in the output images, the new output doesn’t have the passed columns. Those values were dropped since axis was set equal to 1 and the changes were made in the original data frame since inplace was True.
Data Frame
before Dropping Columns-
Data Frame after Dropping Columns-
For more examples refer to Delete columns from DataFrame using Pandas.drop[]
Dealing with Rows:
In order to deal with rows, we can perform basic operations on rows like selecting, deleting, adding and renaming.
Row Selection:
Pandas provide a unique method to retrieve rows from a Data frame.DataFrame.loc[]
method is used to retrieve rows from Pandas DataFrame. Rows can also be selected by passing integer location to an
iloc[] function.
import
pandas as pd
data
=
pd.read_csv[
"nba.csv"
, index_col
=
"Name"
]
first
=
data.loc[
"Avery Bradley"
]
second
=
data.loc[
"R.J. Hunter"
]
print
[first,
"\n\n\n"
, second]
Output:
As shown in the output image, two series were returned since there was only one
parameter both of the times.
For more examples refer to Pandas Extracting rows using .loc[]
Row Addition:
In Order to add a Row in Pandas DataFrame, we can concat the old dataframe with new one.
import
pandas as pd
df
=
pd.read_csv[
"nba.csv"
, index_col
=
"Name"
]
df.head[
10
]
new_row
=
pd.DataFrame[{
'Name'
:
'Geeks'
,
'Team'
:
'Boston'
,
'Number'
:
3
,
'Position'
:
'PG'
,
'Age'
:
33
,
'Height'
:
'6-2'
,
'Weight'
:
189
,
'College'
:
'MIT'
,
'Salary'
:
99999
},
index
=
[
0
]]
df
=
pd.concat[[new_row, df]].reset_index[drop
=
True
]
df.head[
5
]
Output:
Data Frame before Adding Row-
Data Frame after Adding Row-
For more examples refer to Add a row at top in pandas DataFrame
Row Deletion:
In Order to delete a row in Pandas DataFrame, we can use the drop[] method. Rows is deleted by dropping Rows by index label.
import
pandas as pd
data
=
pd.read_csv[
"nba.csv"
, index_col
=
"Name"
]
data.drop[[
"Avery Bradley"
,
"John Holland"
,
"R.J. Hunter"
,
"R.J. Hunter"
], inplace
=
True
]
data
Output:
As shown in the output images, the new output doesn’t have the passed values. Those values were dropped and the changes were made in the original data frame since inplace was True.
Data Frame before Dropping
values-
Data Frame after Dropping values-
For more examples refer to Delete rows from DataFrame using Pandas.drop[]
Problem related to Columns:
- How to get column names in Pandas dataframe
- How to rename columns in Pandas DataFrame
- How to drop one or multiple columns in Pandas Dataframe
- Get unique values from a column in Pandas DataFrame
- How to lowercase column names in Pandas dataframe
- Apply uppercase to a column in Pandas dataframe
- Capitalize first letter of a column in Pandas dataframe
- Get n-largest values from a particular column in Pandas DataFrame
- Get n-smallest values from a particular column in Pandas DataFrame
- Convert a column to row name/index in Pandas
Problem related to Rows:
- Apply function to every row in a Pandas DataFrame
- How to get rows names in Pandas dataframe