Reading Data: CSV, Excel, JSON, and More

Reading Data into Pandas

In practice, data lives in files, databases, or APIs. Pandas can ingest many formats.

CSV files

df_csv = pd.read_csv('data/sales.csv')
print(df_csv.head())

Excel files

df_excel = pd.read_excel('data/financials.xlsx', sheet_name='2023')
print(df_excel.shape)

JSON files

import json
with open('data/users.json') as f:
    data = json.load(f)

df_json = pd.json_normalize(data)
print(df_json.head())

Reading from a URL

titanic_url = "https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv"
df_titanic = pd.read_csv(titanic_url)
print(df_titanic.shape)

Parameters to control parsing

• sep: column delimiter
• parse_dates: column(s) to parse as datetime
• usecols: subset of columns to read
• dtype: enforce column types

df = pd.read_csv('data/sample.csv', sep=';', parse_dates=['date'], dtype={'id': int})