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In this tutorial nosotros will learn how to work with Excel files and Python. It will provide an overview of how to apply Pandas to load and write these spreadsheets to Excel. In the first section, nosotros will become through, with examples, how to read an Excel file, how to read specific columns from a spreadsheet, how to read multiple spreadsheets and combine them to i dataframe, how to read many Excel files, and, finally, how to convert data co-ordinate to specific datatypes (e.g., using Pandas dtypes). When we have done this, we will keep by learning how to write Excel files; how to proper noun the sheets and how to write to multiple sheets.

Before we continue with this read and write Excel files tutorial there is something nosotros demand to do; installing Pandas (and Python, of form, if it'due south not installed). We can install Pandas using Pip, given that we have Pip installed, that is. Come across here how to install pip.

Another great option is to consider is to install the Anaconda Python distribution. This is actually an easy and fast way to get started with information science. No demand to worry most installing the packages you lot need to do computer science separately.

In this section nosotros are going to learn how to read Excel files and spreadsheets to Pandas dataframe objects. All examples in this Pandas Excel tutorial use local files. Annotation, that read_excel also tin can also load Excel files from a URL to a dataframe.  As e'er when working with Pandas, we accept to offset past importing the module:

Now information technology's fourth dimension to learn how to use Pandas read_excel to read in data from an Excel file. The easiest way to use this method is to pass the file proper name as a string. If we don't pass any other parameters, such every bit sheet name, it volition read the showtime sail in the index. In the first example we are not going to apply any parameters:

Here, Pandas read_excel method read the data from the Excel file into a Pandas dataframe object. We so stored this dataframe into a variable called df.

When using read_excel Pandas volition, by default, assign a numeric index or row label to the dataframe, and as usual when int comes to Python, the index will start with zero. We may accept a reason to leave the default index as it is. For instance, if your data doesn't have a column with unique values that tin serve as a better index. In case there is a column that would serve as a amend index, nosotros can override the default behavior .

This is done past setting theindex_col parameter to a column. It takes a numeric value for setting a single column as index or a listing of numeric values for creating a multi-index. In the example below nosotros use the column 'Player' as indices. Notation, these are not unique and it may, thus, not make sense to employ these values as indices.

When using Pandas  read_excel we will automatically get all columns from an Excel files. If we, for some reason, don't desire to parse all columns in the Excel file, we tin use the parameterusecols. Let's say we want to create a dataframe with the columnsPlayer,Salary, andPosition, only. Nosotros tin can do this past adding 1, three, and 4 in a listing:

Co-ordinate to the read_excel documentation we should be able to put in a cord. For instance, cols='Player:Position' should give the states the same results as to a higher place.

If our data has missing values in some cells and these missing values are coded in some way, like "Missing" we can use thena_values parameter.

In the instance below we are using the parameter na_values and we ar putting in a string (i.east., "Missing'):

In in the read excel examples in a higher place we used a dataset that tin be downloaded from this page.

Now we volition acquire how to skip rows when loading an Excel file using Pandas. For this read excel example we will use data that can be downloaded here.

In this example nosotros read the canvas 'session1' which contains  rows that nosotros demand to skip. These rows contains some information nigh the dataset:We will use the parameters sheet_name='Session1′ to read the canvass named 'Session1'. Note, the starting time sheet will be read if we don't employ the sheet_name parameter. In this example the important part is the parameterskiprow=two. We use this to skip the first ii rows:

Nosotros tin can obtain the aforementioned results every bit above using theheader parameter. In the instance Excel file, we apply hither, the third row contains the headers and we will use the parameterheader=two to tell Pandas read_excel that our headers are on the 3rd row.

Our Excel file, example_sheets1.xlsx', has two sheets: 'Session1', and 'Session2.' Each canvass has information for from an imagined experimental session. In the next case we are going to read both sheets, 'Session1' and 'Session2'. Here's how to use Pandas read_excel with multiple sheets:

By using the parameter sheet_name, and a list of names, nosotros volition get an ordered dictionary containing 2 dataframes:

df

Maybe we want to join the data from all sheets (in this case sessions). Merging Pandas dataframes are quite like shooting fish in a barrel. We just use the concat function and loop over the keys (i.east., sheets):

df2 = pd.concat(df[frame] for frame in data.keys())

At present in the example Excel file at that place is a column identifying the dataset (e.one thousand., session number). However, perchance nosotros don't take that kind of information in our Excel file. To merge the ii dataframes and adding a column depicting which session we tin use a for loop:

dfs = [] for framename in data.keys():     temp_df = information[framename]     temp_df['Session'] = framename     dfs.append(temp_df)      df = pd.concat(dfs)

In the lawmaking to a higher place we start by creating a listing and continue past looping through the keys in the listing of dataframes. Finally, we create a temporary dataframe and accept the sheet name and add it in the column 'Session'.

Pandas Read Excel all Sheets

If we want to apply read_excel to load all sheets from an Excel file to a dataframe it is, of ourse, possible. We can fix the parameter sheet_name toNone.

all_sheets_df = pd.read_excel('example_sheets1.xlsx', sheet_name=None)

Reading Many Excel Files

In this department we will learn how to load many files into a Pandas dataframe because, in some cases, nosotros may have a lot of Excel files containing data from, allow'south say, unlike experiments. In Python we can apply the modules os and fnmatch to read all files in a directory. Finally, nosotros apply list comprehension to utilise read_excel on all files we institute:

import bone, fnmatch xlsx_files = fnmatch.filter(os.listdir('.'), '*concat*.xlsx')  dfs = [pd.read_excel(xlsx_file) for xlsx_file in xlsx_files]

If it makes sense we tin, once more, use the office concat to merge the dataframes:

df = pd.concat(dfs, sort=False)

There are other methods to reading many Excel files and merging them. We can, for instance, utilize the module glob:

import glob list_of_xlsx = glob.glob('./*concat*.xlsx')  df = pdf.concat(list_of_xlsx)

Setting the Data type for data or columns

We tin also, if we like, set the data blazon for the columns. Let'south read the example_sheets1.xlsx once again. In the Pandas read_excel example below we use thedtype parameter to prepare the data type of some of the columns.

df = pd.read_excel('example_sheets1.xlsx',sheet_name='Session1',                    header=1,dtype={'Names':str,'ID':str,                                         'Mean':int, 'Session':str})

Nosotros tin can utilise the methodinfo to see the what data types the different columns have:

df.info()

Writing Pandas Dataframes to Excel

Excel files tin, of course, exist created in Python using the module Pandas. In this section of the post we will learn how to create an excel file using Pandas. Nosotros will showtime by creating a dataframe with some variables only first we offset by importing the modules Pandas:

import pandas as pd

The next step is to create the dataframe. We will create the dataframe using a dictionary. The keys will be the column names and the values volition be lists containing our data:

df = pd.DataFrame({'Names':['Andreas', 'George', 'Steve',                            'Sarah', 'Joanna', 'Hanna'],                   'Historic period':[21, 22, xx, xix, 18, 23]})

Then we write the dataframe to an Excel file using the *to_excel* method. In the Pandas to_excel instance beneath we don't use any parameters.

df.to_excel('NamesAndAges.xlsx')

In the output below the consequence of not using any parameters is evident. If nosotros don't use the parametersheet_name we go the default sheet name, 'Sheet1'. We can also run into that nosotros get a new column in our Excel file containing numbers. These are the indices from the dataframe.

If nosotros want our sheet to be named something else and we don't want the index column we tin can do like this:

df.to_excel('NamesAndAges.xlsx', sheet_name='Names and Ages', index=False)

Writing Multiple Pandas Dataframes to an Excel File:

If we happen to accept many dataframes that we want to shop in one Excel file only on different sheets nosotros can do this easily. However, nosotros need to employ ExcelWriter at present:

df1 = pd.DataFrame({'Names': ['Andreas', 'George', 'Steve',                            'Sarah', 'Joanna', 'Hanna'],                    'Age':[21, 22, 20, 19, eighteen, 23]})  df2 = pd.DataFrame({'Names': ['Pete', 'Jordan', 'Gustaf',                            'Sophie', 'Emerge', 'Simone'],                    'Age':[22, 21, xix, 19, 29, 21]})  df3 = pd.DataFrame({'Names': ['Ulrich', 'Donald', 'Jon',                            'Jessica', 'Elisabeth', 'Diana'],                    'Historic period':[21, 21, xx, xix, nineteen, 22]})  dfs = {'Group1':df1, 'Group2':df2, 'Group3':df3} writer = pd.ExcelWriter('NamesAndAges.xlsx', engine='xlsxwriter')  for sheet_name in dfs.keys():     dfs[sheet_name].to_excel(writer, sheet_name=sheet_name, index=False)      writer.save()

In the code above we create three dataframes and and so we continue to put them in a dictionary. Note, the keys are the sheet names and the cell names are the dataframes. Afterward this is done we create a author object using the xlsxwriter engine. We then go on by looping through the keys (i.east., sheet names) and add each sheet. Finally, the file is saved. This is important equally leaving this out will not give yous the intended results.

Summary: How to Work Excel Files using Pandas

That was it! In this mail we accept learned a lot! We have, among other things, learned how to:

• Read Excel files and Spreadsheets using read_excel
  • Load Excel files to dataframes:
    • Read Excel sheets and skip rows
    • Merging many sheets to a dataframe
    • Loading many Excel files into one dataframe
• Write a dataframe to an Excel file
• Taking many dataframes and writing them to one Excel file with many sheets

Exit a comment below if y'all have any requests or suggestions on what should be covered adjacent! Cheque the mail A Basic Pandas Dataframe Tutorial for Beginners to acquire more than about working with Pandas dataframe. That is, later you lot have loaded them from a file (eastward.thou., Excel spreadsheets)

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Source: http://www.pybloggers.com/2018/11/pandas-excel-tutorial-how-to-read-and-write-excel-files/

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