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How to convert data to a JSON format using pandas
AKASH BAJWAOverview
In Python, pandas is specifically designed for data manipulation to assist in data analysis. It offers many built-in data structures and operations to manipulate time series and tabular data. Similarly, DataFrame is a data structure just like tables, but it allows us to perform multiple operations on data.
We use to_json() to convert a DataFrame or Series to a valid
Note: The
datetimeobjects are converted into UNIX timestamps, while NaN values will be converted intonull.
Syntax
# Signature
DataFrame.to_json(path_or_buf=None,
orient=None,
date_format=None,
double_precision=10,
force_ascii=True,
date_unit='ms',
default_handler=None,
lines=False,
compression='infer',
index=True,
indent=None,
storage_options=None)Parameters
path_or_buf: This can be a string, path-like object, orNone. It shows which type of results are returned by this function.orient: This shows the standard JSON format. It can be index, column, split, records, values, or table.date_format: This indicates the date conversion format. It can be epoch or iso.
Return value
Its return values depend on the path_or_buf argument. If path_or_buf=None, it returns false.
Example
# import libraries
import numpy as np
import pandas as pd
# convert a dictionary into a dataframe
df = pd.DataFrame({
"brand": ["Mercedies", "Audi", "Ford"],
"model": ["Benz", "A9" ,"Expedition"],
"year": [2012, 2016, 2020]})
# invoking to_json() to convert data frame to json
json = df.to_json()
# print json string on console
print(json)Converting pandas dataframe to JSON string
Explanation
- Line 2–3: We load the
numpyandpandaslibraries in the program. - Line 5–8: We convert a dictionary of three values into a pandas DataFrame.
- Line 10: We transform the DataFrame content into JSON format using
df.to_json(). - Line 12: We print the JSON string to the console.
RELATED TAGS
json
pandas
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