Pandas read excel slow

As recognized by Pandas creator Wes McKinney himselfit is slow, heavy and using it can be dreadful… But it fulfills many dire needs and the country would collapse without it. For instance, say I have a simple dataframe: one column has words, another has counts of those words in a set of documents.

The table could look something like this:. We want to get rid of this artifact, so for the numbers higher than we subtract This one is, however, slower than watching paint dry. The following is orders of magnitude quicker:.

This makes use of the fact that Pandas columns are actually NumPy arrays. However, it is really its own sub-language, and it has its own idiomatic uses. It is a language for vectorized numerical mathematics. It beautifully expresses mathematical operations on arrays of numbers in a way that comes very close to the expressiveness of mathematics used to model scientific problems in many fields, especially natural sciences and engineering.

For computational scientists in such fields, loops are mental overhead. I do not want to have to build my own roads in order to make the bureaucracy work more smoothly….

Also, it is not the language I expect of a tabular data handling tool. Why should it make sense that column-wise operations are orders of magnitude faster than row-wise operations? I wanted to load the following type of text file into Pandas:. Without going into too much detail, the longer numbers represent words in a text and the short ones at the front represent possible errors e. Essentially this is a key-value store: the keys are the first integers, then comes a separator, and the corresponding value is a list of integers separated by commas.

The list lengths range between one and hundreds of integer numbers and are completely irregular. One could naively load the file as a key value table, for instance like this:. However, I wanted to load it in a way that I could do efficient statistics with it. Doing statistics on an array of lists is again horribly slow. Transforming it to a regular table seems like a good bet, because then we can again make use of the magic and speed of NumPy. This can be done by transforming df as follows:.

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This works, albeit slowly. Just loading a 23MB file took over 3 minutes on my laptop. When I encountered a file of 1. Pandas is not known for its efficient use of memory and in this case it can hardly be blamed, because loading irregular data makes it hard to predict what resources are necessary, making dynamic allocation necessary. That means copying data around the memory banks, which will slow things down a lot. Oh and avoid pointers for the time being.

Oh and use references whenever possible. Activate the environment to run the commands in the rest of this article:. Cookiecutter will now ask you some questions about your project, like its name, your name, etcetera. This contains a number of example functions and the boilerplate of converting them into Python module functions.

In our case, we want to build an efficient way of loading our irregular key-value file. Once that is done, we can easily convert those to a Pandas dataframe in Python itself.

As the Cookiecutter template examples show, NumPy arrays can be defined using the pyarray class. To avoid costly resizing of our arrays, it would be nice if we can calculate the total size we will need for these three arrays.Pandas reading from excel pandas.

To speed it up, we are going to convert the Excel files from. We will need xlsx2csv. Make your python script in the same folder as xlsx2csv.

We will be using glob to get a list of all the xlsx that we want to convert to csv, regex to get filename of the xlsx, and subprocess to call python to run xlsx2csv.

You can see the docs for xlsx2csv.

Slurping Up Excel Data on the Quick: Python, Pandas, and Pickle

So to convert a single xlsx to csvs it will be: python xlsx2csv. Now the cost is frontloaded to converting xlsx to csvs, and it can be slow to convert a folder of xlsx to csvs.

We can speed things up by using multiple threads. Then we will utilize multiprocessing. While the script is running, if you navigate to your file explorer, we will now see that multiple files are being written out to instead of 1 file at a time.

We can see that using multiple threads indeed speed things up. Using 1 thread: Depending on your processor s and how many threads you choose to or can spawn, this will definitely speed things up. Reach out on LinkedIn or email. Getting Started We will need xlsx2csv.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community. Already on GitHub? Sign in to your account. She currently is using numpy. I was aware of benchmarks such as these indicating poor performance of loadtxt vs.

I suggested she try Pandas; she reported it performed worse than loadtxt. This puzzled me, so I did some benchmarks. I duplicated the benchmarks in the SO post linked above, showing Pandas greatly outperforming loadtxt on a many-row, few-column CSV file; this likely rules out a problem with our installations we are both using current Anaconda Python 2.

I also duplicated her poor Pandas performance on the large float file. That unpacks to a benchmark script and two large data files; it's a kB. Perhaps this is just not a use case Pandas was designed to address. Any advice on the fastest way to read such wide CSV files in Python would be appreciated.

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Not suprising at all. Each column individuall get's inferred for its type; this is what takes all the time. Very wide tables are not the usual way of string tabular data. I would simply store your data transposed. I am not sure you are going to get much speeds doing anything else. I was able to make a 1-line change. To have a 2x speedup from the aboveor 3x over existing. However, this may have farther reaching ramifications, eg.

Basically this changes the function for determining how much to read in a single chunk when processing which is an inverse function of the table size. So if someone wants to profile, would appreciate. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. New issue. Jump to bottom. Milestone Contributions Wel Copy link Quote reply.

This comment has been minimized. Sign in to view. Thanks again for looking into this. Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment. Linked pull requests.September 01, 3 min Read. Last week I took part in a Dataviz Battle on the dataisbeautiful subredditwhere we had to create a visualization from the TSA claims dataset.

I like these kind of competitions because most of the time you end up learning a lot of useful things along the way. This time the data was quite clean, but it was scattered across several PDF files and Excel files. In the process of extracting data from PDFs I got to know some tools and libraries, and in the end I used tabula-pya Python wrapper for the Java library tabula.

As for the Excel files, I found out that a one-liner - a simple pd. The solution was to read the file in chunks. The pd. Here is what I did:. Another thing to keep in mind. This dataset contained both.

Please be aware that if your only concern is reading. I remember having used it to create workbooks i. Excel files with many complex worksheets and cell comments. You can even use it to create worksheets with sparklines and VBA macros! These Excel files are too big, so we can't read all rows in one go. We have already read it, so we skip it.Supports xlsxlsxxlsmxlsband odf file extensions read from a local filesystem or URL.

Supports an option to read a single sheet or a list of sheets. Any 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. If you want to pass in a path object, pandas accepts any os. By file-like object, we refer to objects with a read method, such as a file handler e. Strings are used for sheet names. Integers are used in zero-indexed sheet positions. Specify None to get all sheets. Defaults to 0 : 1st sheet as a DataFrame. Row 0-indexed to use for the column labels of the parsed DataFrame.

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If a list of integers is passed those row positions will be combined into a MultiIndex. Use None if there is no header. List of column names to use. Column 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 MultiIndex.

If str, then indicates comma separated list of Excel column letters and column ranges e. Ranges are inclusive of both sides. If callable, then evaluate each column name against it and parse the column if the callable returns True. Data type for data or columns. If io is not a buffer or path, this must be set to identify io. 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.

If dict passed, specific per-column NA values. Whether or not to include the default NaN values when parsing the data.In this tutorial we will learn how to work with Excel files and Python. It will provide an overview of how to use Pandas to load and write these spreadsheets to Excel. In the first section, we will go 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 one dataframe, how to read many Excel files, and, finally, how to convert data according to specific datatypes e.

When we have done this, we will continue by learning how to write Excel files; how to name the sheets and how to write to multiple sheets. We can install Pandas using Pip, given that we have Pip installed, that is.

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See here how to install pip. Another great option is to consider is to install the Anaconda Python distribution. This is really an easy and fast way to get started with computer science. No need to worry about installing the packages you need to do computer science separately. Both of the above methods are explained in this tutorial.

In this section we 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. As always when working with Pandas, we have to start by importing the module:. The easiest way to use this method is to pass the file name as a string. In the first example we are not going to use any parameters:. We then stored this dataframe into a variable called df.

We may have a reason to leave the default index as it is. In case there is a column that would serve as a better index, we can override the default behavior. It takes a numeric value for setting a single column as index or a list of numeric values for creating a multi-index.

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Note, these are not unique and it may, thus, not make sense to use these values as indices. We can do this by adding 1, 3, and 4 in a list:.

pandas read excel slow

In in the read excel examples above we used a dataset that can be downloaded from this page. Now we will learn 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. We use this to skip the first two rows:. Maybe we want to join the data from all sheets in this case sessions.

Merging Pandas dataframes are quite easy. We just use the concat function and loop over the keys i. Now in the example Excel file there is a column identifying the dataset e. To merge the two dataframes and adding a column depicting which session we can use a for loop:.

In the code above we start by creating a list and continue by looping through the keys in the list of dataframes. In Python we can use the modules os and fnmatch to read all files in a directory.Much of the metadata work done by our demographic researchers at the Minnesota Population Center is accomplished on Windows using Microsoft Excel, and then saved to.

Slurping Up Excel Data on the Quick: Python, Pandas, and Pickle

These files can often be VERY large, in some cases approaching 1 million lines. Downstream from this, however, we need more power. When we have tasks that involve e. We do not want to burden our processing time with the overhead of the Excel GUI, nor is this often even particularly feasible. Pandas is an open-source tool for the Python language that provides incredibly fast methods for reading and working with tabular data.

Once in the data frame format, pulling information out is both simple and insanely efficient. If the pickle is fresh, we can load the data frame very quickly into memory via the pickled data frame.

Defining a class to read and store the Excel data as a data frame is the first step to the process. This is the basic logic of an MpcSpreadsheet object:. A couple of things to note here. The object is initialized with a path to the Excel workbook, and the project directory path more on that later.

As a helpful convention and forced standardization, we uppercase and strip trailing whitespace from all of our column headings Row 1 of the spreadsheet.

pandas read excel slow

This is to avoid issues of header formatting when comparing two worksheets, each with a comparable column of data. So, this class defines a Python object that reads an Excel worksheet into Pandas.

pandas read excel slow

Once in Pandas, we can access information really quickly, but how much overhead is there in getting the data from Excel to Pandas? Benchmark 2 of reading in 1 very large Excel file:. Parsing in the data from the xlsx format is slow. Following a basic understanding of pickling Python objects to disk, the first thing needed is a strategy for caching the pickled data frames.

There are lots of potential organizational schemes for where the pickles should go.

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For example, right alongside the excel files is one option. Instead, a completely mirrored structure within a. To build this structure we add several methods to our MpcSpreadsheet class:. The biggest gain is in the large file read, where the 50s read is reduced to about 1.

pandas read excel slow

However, even reading 1, smaller files reduces the time required by 3x, from 1. Each child class provides class-specific methods for efficiently accessing information directly from the data frame.

The only expectation of the child class is that there is a data frame initialized that is the exact representation of the spreadsheet. After all, if the pickle is fresh, they are going to be identical. Summing it all up: without doing anything particularly groundbreaking, great utility can be had using the standard libraries Python provides.

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For simplification in the article I am treating all Excel workbooks as single worksheet workbooks. The two hardest things in programming: Naming things, cache validation, and off-by-one errors. Tackling the Excel Overhead Problem Much of the metadata work done by our demographic researchers at the Minnesota Population Center is accomplished on Windows using Microsoft Excel, and then saved to.

Defining a Spreadsheet Class Defining a class to read and store the Excel data as a data frame is the first step to the process.

All spreadsheet classes in the ipums.


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