read_excel pandas code example

Example 1: pandas read excel

import pandas as pd
pd.read_excel('tmp.xlsx’, sheet_name='Sheet1')

Example 2: pandas read excel

import pandas as pd
pd.read_excel('tmp.xlsx', index_col=0)

Example 3: import excel file in python pandas

import pandas as pd

df = pd.read_excel (r'Path where the Excel file is stored\File name.xlsx')

Example 4: pandas read from excel

import pandas as pd
pandas.read_excel(io, sheet_name=0, header=0, names=None,
                  index_col=None, usecols=None, 
                  squeeze=False, dtype=None, engine=None, 
                  converters=None, true_values=None, 
                  false_values=None, skiprows=None, 
                  nrows=None, na_values=None, 
                  keep_default_na=True, verbose=False, 
                  parse_dates=False, date_parser=None, 
                  thousands=None, comment=None, 
                  skipfooter=0, convert_float=True, 
                  mangle_dupe_cols=True, **kwds)

# Example
pd.read_excel('tmp.xlsx', index_col=0)

Example 5: pandas excel python

import pandas as pd

# reading in from external file
movies = pd.read_excel('movies.xls')

# prints first five rows 
movies.head(n=5)
# prints last five rows
movies.tail(n=5)

# for excel files w/ multiple sheets
movies_sheet1 = pd.read_excel(excel_file, sheetname=0)
movies_sheet2 = pd.read_excel(excel_file, sheetname=1)
movies_sheet2 = pd.read_excel(excel_file, sheetname='name of third sheet')

# sort by column
sorted_by_gross = movies.sort_values(['Gross Earnings'], ascending=False)

# exporting to file
movies.to_excel('output.xlsx')

Example 6: read excel file using pandas in python

import sqlite3
import pandas as pd
from sqlalchemy import create_engine

# Read Excel file or sheets using pandas

# Setup code :)

file = "location of Excel file..."

engine = create_engine('sqlite://', echo=False)
df = pd.read_excel(file, sheet_name=sheetname)
df.to_sql("test", engine, if_exists="replace", index=False)
results = engine.execute("Select * from test")
final = pd.DataFrame(results, columns=df.columns)