How do you read excel files in python using pandas?
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You can easily import an Excel file into Python using Pandas. In order to accomplish this goal, you’ll need to use read_excel. Show
In this short guide, you’ll see the steps to import an Excel file into Python using a simple example. But before we start, here is a template that you may use in Python to import your Excel file: import pandas as pd df = pd.read_excel (r'Path where the Excel file is stored\File name.xlsx') print (df) Note that for an earlier version of Excel, you may need to use the file extension of ‘xls’ And if you have a specific Excel sheet that you’d like to import, you may then apply: import pandas as pd df = pd.read_excel (r'Path where the Excel file is stored\File name.xlsx', sheet_name='your Excel sheet name') print (df) Let’s now review an example that includes the data to be imported into Python. The Data to be Imported into PythonSuppose that you have the following table stored in Excel (where the Excel file name is ‘Product List‘):
How would you then import the above data into Python? You may follow the steps below to import an Excel file into Python. Step 1: Capture the file pathFirst, you’ll need to capture the full path where the Excel file is stored on your computer. For example, let’s suppose that an Excel file is stored under the following path: C:\Users\Ron\Desktop\Product List.xlsx In the Python code, to be provided below, you’ll need to modify the path name to reflect the location where the Excel file is stored on your computer. Don’t forget to include the file name (in our example, it’s ‘Product list‘ as highlighted in blue). You’ll also need to include the Excel file extension (in our case, it’s ‘.xlsx‘ as highlighted in green). Step 2: Apply the Python codeAnd here is the Python code tailored to our example. Additional notes are included within the code to clarify some of the components used. import pandas as pd df = pd.read_excel (r'C:\Users\Ron\Desktop\Product List.xlsx') #place "r" before the path string to address special character, such as '\'. Don't forget to put the file name at the end of the path + '.xlsx' print (df) Step 3: Run the Python code to import the Excel fileRun the Python code (adjusted to your path), and you’ll get the following dataset: Notice that we got the same results as those that were stored in the Excel file. Note: you will have to install an additional package if you get the following error when running the code: ImportError: Missing optional dependency ‘xlrd’ You may then use the PIP install approach to install openpyxl for .xlsx files: pip install openpyxl Optional Step: Selecting subset of columnsNow what if you want to select a specific column or columns from the Excel file? For example, what if you want to select only the Product column? If that’s the case, you can specify this column name as captured below: import pandas as pd data = pd.read_excel (r'C:\Users\Ron\Desktop\Product List.xlsx') df = pd.DataFrame(data, columns= ['Product']) print (df) Run the code (after adjusting the file path), and you’ll get only the Product column: You can specify additional columns by separating their names using a comma, so if you want to include both the Product and Price columns, you can use this syntax: import pandas as pd data = pd.read_excel (r'C:\Users\Ron\Desktop\Product List.xlsx') df = pd.DataFrame(data, columns= ['Product','Price']) print (df) You’ll need to make sure that the column names specified in the code exactly match with the column names within the Excel file. Otherwise, you’ll get NaN values. ConclusionYou just saw how to import an Excel file into Python using Pandas. At times, you may need to import a CSV file into Python. If that’s the case, you may want to check the following tutorial that explains how to import a CSV file into Python using Pandas. You may also check the Pandas Documentation to find out more about the different options that you may apply in regards to read_excel. Read an Excel file into a pandas DataFrame. Supports xls, xlsx, xlsm, xlsb, odf, ods and odt file extensions read from a local filesystem or URL. Supports an option to read a single sheet or a list of sheets. Parametersiostr, bytes, ExcelFile, xlrd.Book, path object, or file-like objectAny valid string path is acceptable. The string could be a URL. Valid URL schemes include http, ftp, s3,
and file. For file URLs, a host is expected. A local file could be: If you want to pass in a path object, pandas accepts any By file-like object, we refer to objects with a Strings are used for sheet names. Integers are used in zero-indexed sheet positions (chart sheets do not count as a sheet position). Lists of strings/integers are used to request multiple sheets. Specify None to get all worksheets. Available cases:
Row (0-indexed) to use for the column labels of the parsed DataFrame. If a list of integers is passed those row positions will be combined into a List of column names to use. If file contains no header row, then you should explicitly pass header=None. index_colint, list of int, default NoneColumn (0-indexed) to use as the row labels of the DataFrame. Pass None if there is no such column. If a list is passed, those columns will be combined into a
Returns a subset of the columns according to behavior above. squeezebool, default FalseIf the parsed data only contains one column then return a Series. Deprecated since version 1.4.0: Append Data type for data or columns. E.g. {‘a’: np.float64, ‘b’: np.int32} Use object to preserve data as stored in Excel and not interpret dtype. If converters are specified, they will be applied INSTEAD of dtype conversion. enginestr, default NoneIf io is not a buffer or path, this must be set to identify io. Supported engines: “xlrd”, “openpyxl”, “odf”, “pyxlsb”. Engine compatibility :
Changed in version 1.2.0: The engine xlrd now only supports old-style
Dict of functions for converting values in certain columns. Keys can either be integers or column labels, values are functions that take one input argument, the Excel cell content, and return the transformed content. true_valueslist, default NoneValues to consider as True. false_valueslist, default NoneValues to consider as False. skiprowslist-like, int, or callable, optionalLine numbers to skip (0-indexed) or number of lines to skip (int) at the start of the file. If callable, the callable
function will be evaluated against the row indices, returning True if the row should be skipped and False otherwise. An example of a valid callable argument would be Number of rows to parse. na_valuesscalar, str, list-like, or dict, default NoneAdditional strings to recognize as NA/NaN. If dict passed, specific per-column NA values. By default the following
values are interpreted as NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘ Whether or not to include the default NaN values when parsing the data. Depending on whether na_values is passed in, the behavior is as follows:
Note that if na_filter is passed in as False, the keep_default_na and na_values parameters will be ignored. na_filterbool, default TrueDetect missing value markers (empty strings and the value of na_values). In data without any NAs, passing na_filter=False can improve the performance of reading a large file. verbosebool, default FalseIndicate number of NA values placed in non-numeric columns. parse_datesbool, list-like, or dict, default FalseThe behavior is as follows:
If a column or index contains an unparsable date, the entire column or index will be returned unaltered as an object data type. If you don`t want to parse some cells as date just change their type in Excel to “Text”. For non-standard datetime parsing, use Note: A fast-path exists for iso8601-formatted dates. Function to use for converting a sequence of string columns to an array of datetime instances. The default uses Thousands separator for parsing string columns to numeric. Note that this parameter is only necessary for columns stored as TEXT in Excel, any numeric columns will automatically be parsed, regardless of display format. decimalstr, default ‘.’Character to recognize as decimal point for parsing string columns to numeric. Note that this parameter is only necessary for columns stored as TEXT in Excel, any numeric columns will automatically be parsed, regardless of display format.(e.g. use ‘,’ for European data). New in version 1.4.0. commentstr, default NoneComments out remainder of line. Pass a character or characters to this argument to indicate comments in the input file. Any data between the comment string and the end of the current line is ignored. skipfooterint, default 0Rows at the end to skip (0-indexed). convert_floatbool, default TrueConvert integral floats to int (i.e., 1.0 –> 1). If False, all numeric data will be read in as floats: Excel stores all numbers as floats internally. Deprecated since version 1.3.0: convert_float will be removed in a future version mangle_dupe_colsbool, default TrueDuplicate columns will be specified as ‘X’, ‘X.1’, …’X.N’, rather than ‘X’…’X’. Passing in False will cause data to be overwritten if there are duplicate names in the columns. storage_optionsdict, optionalExtra options that make sense for a particular storage connection, e.g. host, port, username, password, etc., if using a URL that will be parsed by New in version 1.2.0. ReturnsDataFrame or dict of DataFramesDataFrame from the passed in Excel file. See notes in sheet_name argument for more information on when a dict of DataFrames is returned. See also DataFrame.to_excelWrite DataFrame to an Excel file. DataFrame.to_csvWrite DataFrame to a comma-separated values (csv) file. read_csvRead a comma-separated values (csv) file into DataFrame. read_fwfRead a table of fixed-width formatted lines into DataFrame. Examples The file can be read using the file name as string or an open file object: >>> pd.read_excel('tmp.xlsx', index_col=0) Name Value 0 string1 1 1 string2 2 2 #Comment 3 >>> pd.read_excel(open('tmp.xlsx', 'rb'), ... sheet_name='Sheet3') Unnamed: 0 Name Value 0 0 string1 1 1 1 string2 2 2 2 #Comment 3 Index and header can be specified via the index_col and header arguments >>> pd.read_excel('tmp.xlsx', index_col=None, header=None) 0 1 2 0 NaN Name Value 1 0.0 string1 1 2 1.0 string2 2 3 2.0 #Comment 3 Column types are inferred but can be explicitly specified >>> pd.read_excel('tmp.xlsx', index_col=0, ... dtype={'Name': str, 'Value': float}) Name Value 0 string1 1.0 1 string2 2.0 2 #Comment 3.0 True, False, and NA values, and thousands separators have defaults, but can be explicitly specified, too. Supply the values you would like as strings or lists of strings! >>> pd.read_excel('tmp.xlsx', index_col=0, ... na_values=['string1', 'string2']) Name Value 0 NaN 1 1 NaN 2 2 #Comment 3 Comment lines in the excel input file can be skipped using the comment kwarg >>> pd.read_excel('tmp.xlsx', index_col=0, comment='#') Name Value 0 string1 1.0 1 string2 2.0 2 None NaN How do you read and write Excel file in Python using pandas?Pandas: How to Read and Write Files. Installing Pandas.. Preparing Data.. Using the Pandas read_csv() and .to_csv() Functions. Write a CSV File. ... . Using Pandas to Write and Read Excel Files. Write an Excel File. ... . Understanding the Pandas IO API. Write Files. ... . Working With Different File Types. CSV Files. ... . Working With Big Data. ... . Conclusion.. How do I read an entire Excel file in Python?# Import the xlrd module.. import xlrd.. # Define the location of the file.. loc = ("path of file"). # To open the Workbook.. wb = xlrd.open_workbook(loc). sheet = wb.sheet_by_index(0). # For row 0 and column 0.. Can pandas read an open Excel file?We can use the pandas module read_excel() function to read the excel file data into a DataFrame object. If you look at an excel sheet, it's a two-dimensional table. The DataFrame object also represents a two-dimensional tabular data structure.
How do I read an XLSX file in Python?The read_excel() function of pandas is used for reading the xlsx file. This function has used in the script to read the sales. xlsx file. The DataFrame() function has used here to read the content of the xlsx file in the data frame and store the values in the variable named data.
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