In This Article, You Will Learn About Python Pandas Read
Python Pandas Read – Before moving ahead, let’s know about Pandas DataFrame
Table of Contents
Pandas Read CSV
One method to store large data sets is using CSV Files (comma-separated CSV files).
CSV files are plain text and are a standard format that anyone, even Pandas can comprehend.
We will be using a CSV file called ‘data.csv’.
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Example – Loading the csv file into a DataFrame.
import pandas as pd
file = pd.read_csv('data.csv')
print(file.to_string())
As it is shown above, it returned the data loaded into the file.
Example – Print a sample of csv file.
import pandas as pd
df = pd.read_csv('data.csv')
print(df)
As it is shown above, it returned the data loaded into the file.
Pandas Read JSON
Big data sets are usually stored or extracted in JSON.
JSON is a plain text however, it has the structure of an object and is widely known in the programming world, such as Pandas.
We will be using a JSON file called ‘data.json’.
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Example – Loading the JSON file into a DataFrame.
import pandas as pd
file = pd.read_json('data.json')
print(file.to_string())
Note: To print the entire DataFrame, use to_string()
Dictionary as JSON
A JSON object is as equal as Python Dictionary’s formate.
In the event that your JSON code isn’t in the form of a file, but instead inside the form of a Python Dictionary, you can transfer it to DataFrame directly.
Example – Loading a Python Dictionary into a DataFrame.
import pandas as pd
details = {
"Name": {"A": 10, "B": 20, "C": 30},
"ID": {'A': 40, 'B': 50, 'C': 60},
"Roll Number": {'A': 70, 'B': 80, 'C': 90}
}
data = pd.DataFrame(details)
print(data)
If you find anything incorrect in the above-discussed topic and have any further questions, please comment below.
