How do i read a python file in pandas?
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Pandas is a powerful and flexible Python package that allows you to work with labeled and time
series data. It also provides statistics methods, enables plotting, and more. One crucial feature of Pandas is its ability to write and read Excel, CSV, and many other types of files. Functions like the Pandas In this tutorial, you’ll learn:
Let’s start reading and writing files! Installing PandasThe code in this tutorial is executed with CPython 3.7.4 and Pandas 0.25.1. It would be beneficial to make sure you have the latest versions of Python and Pandas on your machine. You might want to create a new virtual environment and install the dependencies for this tutorial. First, you’ll need the Pandas library. You may already have it installed. If you don’t, then you can install it with pip: Once the installation process completes, you should have Pandas installed and ready. Anaconda is an excellent Python distribution that comes with Python, many useful packages like Pandas, and a package and environment manager called Conda. To learn more about Anaconda, check out Setting Up Python for Machine Learning on Windows. If you don’t have Pandas in your virtual environment, then you can install it with Conda: Conda is powerful as it manages the dependencies and their versions. To learn more about working with Conda, you can check out the official documentation. Preparing DataIn this tutorial, you’ll use the data related to 20 countries. Here’s an overview of the data and sources you’ll be working with:
This is how the data looks as a table:
You may notice that some of the data is missing. For example, the continent for Russia is not specified because it spreads across both Europe and Asia. There are also several missing independence days because the data source omits them. You can organize this data in Python using a nested dictionary:
Each row of the table is written as an inner dictionary whose keys are the column names and values are the corresponding data. These dictionaries are then collected as the values in the outer You can use this >>>
Now that you have Pandas imported, you can use the
>>>
Now you have your Versions of Python older than 3.6 did not guarantee the order of keys in
dictionaries. To ensure the order of columns is maintained for older versions of Python and Pandas, you can specify >>>
Now that you’ve prepared your data, you’re ready to start working with files! Using the Pandas read_csv() and .to_csv() FunctionsA comma-separated values (CSV) file is a plaintext file with a Write a CSV FileYou can save your Pandas >>>
That’s it! You’ve created the file
This text file contains
the data separated with commas. The first column contains the row labels. In some cases, you’ll find them irrelevant. If you don’t want to keep them, then you can pass the argument Read a CSV FileOnce your data is saved in a CSV file, you’ll likely want to load and use it from time to
time. You can do that with the Pandas >>>
In this case, the Pandas The parameter You’ll learn more about using Pandas with CSV files later on in this tutorial. You can also check out Reading and Writing CSV Files in Python to see how to handle CSV files with the built-in Python library csv as well. Using Pandas to Write and Read Excel FilesMicrosoft Excel is probably the most widely-used spreadsheet software. While older versions used binary
You can install them using pip with a single command:
You can also use Conda:
Please note that you don’t have to install all these packages. For example, you
don’t need both openpyxl and XlsxWriter. If you’re going to work just with Write an Excel FileOnce you have those packages installed, you can save your >>>
The argument The first column of the file contains the labels of the rows, while the other columns store data. Read an Excel FileYou can load data from Excel files with
>>>
You’ll learn more about working with Excel files later on in this tutorial. You can also check out Using Pandas to Read Large Excel Files in Python. Understanding the Pandas IO APIPandas IO Tools is the API that allows you to save the contents of Write Files
You’ve learned about
There are still more file types that you can write to, so this list is not exhaustive. These methods have parameters specifying the target file path where you saved the data and labels. This is mandatory in some cases and optional in others. If this option is available and you choose to omit it, then the methods return the objects (like strings or iterables) with the contents of The optional parameter Read FilesPandas functions for
reading the contents of files are named using the pattern
These functions have a parameter that specifies the target file path. It can be any valid string that represents the path, either on a local machine or in a URL. Other objects are also acceptable depending on the file type. The optional parameter Working With Different File TypesThe Pandas library offers a wide range of possibilities for saving your data to files and loading data from files. In this section, you’ll learn more about working with CSV and Excel files. You’ll also see how to use other types of files, like JSON, web pages, databases, and Python pickle files. CSV FilesYou’ve already learned how to read and write CSV files. Now let’s dig a little deeper into the details. When you use
>>>
Now you have the string The continent that corresponds to Russia in >>>
This example uses When you save your >>>
This code produces the file
Now, the string When Pandas reads files, it considers the empty string (
If you don’t want this behavior, then you can pass >>>
Here, you’ve marked the string When you load data from a file, Pandas assigns the data types to the values of each column by default. You can
check these types with >>>
The columns with strings and dates ( You can use the parameter >>>
Now, you have 32-bit floating-point numbers ( Now that you have real dates, you can save them in the format you like: >>>
Here, you’ve specified the parameter
The format of the dates is different now.
The format There are several other optional parameters that you can use with
Here’s how you would pass arguments for >>>
The data is separated with a semicolon ( The Pandas JSON FilesJSON stands for JavaScript object notation. JSON files are plaintext files used for data interchange, and humans can read them easily. They follow the ISO/IEC 21778:2017 and
ECMA-404 standards and use the You can save the data from your >>>
This code produces the file
You can get a different file structure if you pass an argument for the optional parameter >>>
The You should get a new
file
There are few more options for >>>
This code should yield the file
You can get another interesting file structure with >>>
The resulting file is
If you don’t provide the value for the optional parameter There are other optional parameters you can use. For instance, you can set >>>
In this example, you’ve created the
In this file, you have large integers instead of dates for the independence days. That’s because the default value of the optional
parameter However, if you pass >>>
This code produces the following JSON file:
The dates in the resulting file are in the ISO 8601 format. You can load the data from a JSON file with >>>
The parameter There are other optional parameters you can use as well:
Note that you might lose the order of rows and columns when using the JSON format to store your data. HTML FilesAn HTML is a plaintext file that uses
hypertext markup language to help browsers render web pages. The extensions for HTML files are
You can also use Conda to install the same packages:
Once you have these libraries, you can save the contents
of your >>>
This code generates a file
This file shows the
Here are some other optional parameters:
You use parameters like these to specify different aspects of the resulting files or strings. You can create a >>>
This is very similar to what you did when reading CSV files. You also have parameters that help you work with dates, missing values, precision, encoding, HTML parsers, and more. Excel FilesYou’ve already learned how to read and
write Excel files with Pandas. However, there are a few more options worth considering. For one, when you use >>>
Here, you create a file The optional parameters >>>
Here, you specify that the table should start in the third row and the fifth column. You also used zero-based indexing, so the third row is denoted by Now the resulting worksheet looks like this: As you can see, the table starts in the third row
Here’s how you would use this parameter in your code: >>>
Both statements above create the same There are other optional parameters you can use with SQL FilesPandas IO tools can also read and write databases. In this next example, you’ll write your data to a database called You can install SQLAlchemy with pip: You can also install it with Conda:
Once you have SQLAlchemy installed, import >>>
Now that you have everything set up, the next step is to create a >>>
Once you’ve created your
>>>
The parameter You should get the database The first column contains the row labels. To omit writing them into the database, pass There are a few more optional parameters. For example, you can use
You can load the data from the database with
>>>
The parameter >>>
Now you have the same Note that the continent for Russia is now >>>
Also note that you didn’t have to pass There are other functions that you can use to read databases, like
Pickle FilesPickling is the act of converting Python objects into byte streams. Unpickling is the inverse process. Python pickle files are the binary files that keep the data and hierarchy of Python objects. They usually have the extension You can save your >>>
Like you did with databases, it can be convenient first to specify the data types. Then, you create a file You can get the data from a pickle file with >>>
>>>
These are the same ones that you specified before using As a word of caution, you should always beware of loading pickles from untrusted sources. This can be dangerous! When you unpickle an untrustworthy file, it could execute arbitrary code on your machine, gain remote access to your computer, or otherwise exploit your device in other ways. Working With Big DataIf your files are too large for saving or processing, then there are several approaches you can take to reduce the required disk space:
You’ll take a look at each of these techniques in turn. Compress and Decompress FilesYou can create an archive file like you would a regular one, with the addition of a suffix that corresponds to the desired compression type:
Pandas can deduce the compression type by itself:
>>>
Here, you create a compressed You can open this compressed file as usual with the Pandas >>>
You can specify the type of compression with the optional parameter
The default value Here’s how you would compress a pickle file: >>>
You should get the file >>>
You can give the other compression methods a try, as well. If you’re using pickle files, then keep in mind that the Choose ColumnsThe Pandas >>>
Now
you have a Instead of the column names, you can also pass their indices: >>>
Expand the code block below to compare these results with the file
You can see the following columns:
Simlarly, >>>
Again, the Omit RowsWhen you test an algorithm for data processing or machine learning, you often don’t need the
entire dataset. It’s convenient to load only a subset of the data to speed up the process. The Pandas
Here’s how you would skip rows with odd zero-based indices, keeping the even ones: >>>
In this example, If you want to choose rows randomly, then Force Less Precise Data TypesIf you’re okay with less precise data types, then you can potentially save a
significant amount of memory! First, get the data types with >>>
The columns with the floating-point numbers are 64-bit floats. Each number of this type >>>
>>>
This example shows how you can combine the numeric columns You can also extract the data values in the form of a NumPy array with >>>
The result is the same 480 bytes. So, how do you save memory? In this case, you can specify that your numeric columns >>>
The dictionary Now you can verify that each numeric column needs 80 bytes, or 4 bytes per item: >>>
Each value is
a floating-point number of 32 bits or 4 bytes. The three numeric columns contain 20 items each. In total, you’ll need 240 bytes of memory when you work with the type In addition to saving memory, you can significantly reduce the time required to process data by using Use Chunks to Iterate Through FilesAnother way to deal with very large datasets is to split the data into smaller chunks and process one chunk at a time. If you use >>>
>>>
In this example, the In each iteration, you get and process the ConclusionYou now know how to save the data and labels from Pandas You’ve used the Pandas You’ve also learned how to save time, memory, and disk space when working with large data files:
You’ve mastered a significant step in the machine learning and data science process! If you have any questions or comments, then please put them in the comments section below. Watch Now This tutorial has a related video course created by the Real Python team. Watch it together with the written tutorial to deepen your understanding: Reading and Writing Files With Pandas How do I load a file into pandas?Steps to Import a CSV File into Python using Pandas. Step 1: Capture the File Path. Firstly, capture the full path where your CSV file is stored. ... . Step 2: Apply the Python code. ... . Step 3: Run the Code. ... . Optional Step: Select Subset of Columns.. What type of files can pandas read?What file formats can pandas use?. Comma-separated values (CSV). Plain Text (txt). Images.. How do I read a .text file in pandas?Use pd.
read_csv(file) with the path name of a text file as file to return a pd. DataFrame with the data from the text file. Further reading: Read more about pd. read_csv() in the Pandas documentation.
How do you read a dataset in Python?As a beginner, you might only know a single way to load data (normally in CSV) which is to read it using pandas. read_csv function.. Manual function.. loadtxt function.. genfromtxt function.. read_csv function.. Pickle.. |