How do you read CSV, Excel, and JSON files into a Pandas DataFrame?

import pandas as pd

# --- CSV ---
df = pd.read_csv('sales.csv')
# Common options:
df = pd.read_csv(
    'sales.csv',
    sep=';',              # custom delimiter (semicolon, tab, etc.)
    header=0,             # row to use as column names (0 = first row)
    index_col='order_id', # use this column as the row index
    usecols=['date', 'amount', 'region'],  # read only these columns
    dtype={'amount': 'float32'},            # explicit dtype
    parse_dates=['date'],                   # auto-parse date strings
    na_values=['N/A', '--', ''],            # treat as NaN
    nrows=1000,           # read only first 1000 rows (useful for large files)
    encoding='utf-8',
)

# --- Excel ---
df_xl = pd.read_excel('report.xlsx', sheet_name='Q1', skiprows=2)

# --- JSON ---
df_j = pd.read_json('data.json', orient='records')
# orient='records' expects [{...}, {...}] — the common API response shape

# --- SQL ---
import sqlite3
conn = sqlite3.connect('mydb.sqlite')
df_sql = pd.read_sql('SELECT * FROM orders WHERE amount > 100', conn)

# Always inspect after reading
print(df.shape)
print(df.head())
print(df.dtypes)
print(df.info())   # shows non-null counts per column