In this article, we will discuss how to read text files with pandas in python. In python, the pandas module allows us to load DataFrames from external files and work on them. The dataset can be in different types of files.
Text File Used:
Method 1: Using read_csv()
We will read the text file with pandas using the read_csv() function. Along with the text file, we also pass separator as a single space (‘ ’) for the space character because, for text files, the space character will separate each field. There are three parameters we can pass to the read_csv() function.
Syntax:
data=pandas.read_csv(‘filename.txt’, sep=’ ‘, header=None, names=[“Column1”, “Column2”])
Parameters:
- filename.txt: As the name suggests it is the name of the text file from which we want to read data.
- sep: It is a separator field. In the text file, we use the space character(‘ ‘) as the separator.
- header: This is an optional field. By default, it will take the first line of the text file as a header. If we use header=None then it will create the header.
- names: We can assign column names while importing the text file by using the names argument.
Example 1:
Python3
# Read Text Files with Pandas using read_csv() # importing pandasimport pandas as pd # read text file into pandas DataFramedf = pd.read_csv("gfg.txt", sep=" ") # display DataFrameprint(df) |
Output:
Example 2:
In example 2 we will make the header filed equal to None. This will create a default header in the output. And take the first line of the text file as data entry. The created header name will be a number starting from 0.
Python3
# Read Text Files with Pandas using read_csv() # importing pandasimport pandas as pd # read text file into pandas DataFrame and# create headerdf = pd.read_csv("gfg.txt", sep=" ", header=None) # display DataFrameprint(df) |
Output:
Example 3:
In the above output, we can see it creates a header starting from number 0. But we can also give names to the header. In this example, we will see how to create a header with a name using pandas.
Python3
# Read Text Files with Pandas using read_csv() # importing pandasimport pandas as pd # read text file into pandas DataFrame and create # header with namesdf = pd.read_csv("gfg.txt", sep=" ", header=None, names=["Team1", "Team2"]) # display DataFrameprint(df) |
Output:
Method 2: Using read_table()
We can read data from a text file using read_table() in pandas. This function reads a general delimited file to a DataFrame object. This function is essentially the same as the read_csv() function but with the delimiter = ‘\t’, instead of a comma by default. We will read data with the read_table function making separator equal to a single space(‘ ‘).
Syntax:
data=pandas.read_table('filename.txt', delimiter = ' ')
Example:
Python3
# Read Text Files with Pandas using read_table() # importing pandasimport pandas as pd # read text file into pandas DataFramedf = pd.read_table("gfg.txt", delimiter=" ") # display DataFrameprint(df) |
Output:
Method 3: Using read_fwf()
The fwf in the read_fwf() function stands for fixed-width lines. We can use this function to load DataFrames from files. This function also supports text files. We will read data from the text files using the read_fef() function with pandas. It also supports optionally iterating or breaking the file into chunks. Since the columns in the text file were separated with a fixed width, this read_fef() read the contents effectively into separate columns.
Syntax:
data=pandas.read_fwf('filename.txt')
Example:
Python3
# Read Text Files with Pandas using read_fwf() # importing pandasimport pandas as pd # read text file into pandas DataFramedf = pd.read_fwf("gfg.txt") # display DataFrameprint(df) |
Output:
