Data Analysis: Loading Data from CSV & Excel Files in Jupyter Notebook
The first stage of data analysis is getting the data. Today with this tutorial we will see an easy and fast way to import our data in Jupyter Notebook.
To loading our CSV files or excel files, we will use Jupyter. If you don’t know what is it Jupyter, don’t worry; you can find some information about in my previous article click here Anaconda&Jupyter.
Thanks to Jupyter and Anaconda, with just a few lines of code, we will be able to import data
in the following formats:
- CSV
- Excel
- SQL (I will explain this in the next article)
Let’s see how to:
Import CSV Data:
Suppose to have a csv file with name data, you need to write this 4 code lines to get you data
Import Excel Data:
The method to import Excel data in Jupyter it’s really similar like as the method to import CSV data.
That’s all my friends !
In the next article we will see how to save data in CSV and Excel files, and how to combine Excel file.
How To Read CSV Files In a Jupyter Notebook Online
As a data scientist, one of the most common tasks you’ll encounter is reading data from CSV files. These files are widely used to store tabular data, and they can be easily created and manipulated using spreadsheet software like Microsoft Excel or Google Sheets. However, when working with large datasets, it’s often more convenient to use a programming language like Python and a tool like Jupyter Notebook. You can use Jupyter notebooks for free online at Saturn Cloud.
Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It supports many programming languages, including Python, R, and Julia, and it’s widely used in data science, scientific research, and education.
Struggling with reading CSV files in Jupyter Notebook online? Simplify your data science tasks with Saturn Cloud. Begin your free trial today and experience seamless file handling!
In this tutorial, we’ll show you how to read a CSV file in Jupyter Notebook online using Python and the Pandas library. Pandas is a powerful data manipulation library that provides easy-to-use data structures and data analysis tools for Python.
Step 1: Import the Pandas library
To use the Pandas library, you need to import it into your Jupyter Notebook. You can do this by running the following command:
This command imports the Pandas library and assigns it the alias “pd”, which is a common convention in the Python community.
Step 2: Load the CSV file
To load a CSV file into Pandas, you can use the read_csv() function. This function takes the path to the CSV file as a parameter and returns a DataFrame object, which is a two-dimensional table-like data structure that can hold data of different types.
Assuming that your CSV file is stored in the same directory as your Jupyter Notebook, you can load it by running the following command:
This command reads the CSV file named “mydata.csv” and stores its contents in a DataFrame object named “df”. You can replace “mydata.csv” with the name of your CSV file.
If your CSV file is stored in a different directory, you need to provide the full path to the file. For example, if your CSV file is stored in the “data” directory of your Jupyter Notebook, you can load it by running the following command:
This command reads the CSV file named “mydata.csv” from the “data” directory and stores its contents in a DataFrame object named “df”.
Step 3: Explore the data
Once you’ve loaded the CSV file into a DataFrame object, you can start exploring its contents. Pandas provides many functions and methods for data manipulation, aggregation, and visualization.
For example, you can use the head() function to display the first five rows of the DataFrame:
This command displays the first five rows of the DataFrame. You can change the number of rows displayed by passing a parameter to the head() function. For example, to display the first ten rows, you can run:
You can also use the describe() function to get a statistical summary of the DataFrame:
This command displays the count, mean, standard deviation, minimum, and maximum values for each column of the DataFrame. If your DataFrame contains non-numeric columns, the describe() function will skip them.
Step 4: Manipulate the data
Pandas provides many functions and methods for manipulating the data in a DataFrame. For example, you can use the loc[] operator to select rows and columns based on their labels:
This command selects the first six rows of the DataFrame and the columns named “column1” and “column2”. You can replace “column1” and “column2” with the names of your columns.
You can also use the iloc[] operator to select rows and columns based on their positions:
This command selects the first six rows of the DataFrame and the first two columns.
Step 5: Visualize the data
Pandas provides many functions and methods for visualizing the data in a DataFrame. For example, you can use the plot() function to create a line plot of a column:
This command creates a line plot of the column named “column1”. You can replace “column1” with the name of your column.
You can also use the scatter() function to create a scatter plot of two columns:
This command creates a scatter plot of the columns named “column1” and “column2”. You can replace “column1” and “column2” with the names of your columns.
Struggling with reading CSV files in Jupyter Notebook online? Simplify your data science tasks with Saturn Cloud. Begin your free trial today and experience seamless file handling!
Conclusion
In this tutorial, we’ve shown you how to read a CSV file in Jupyter Notebook online using Python and the Pandas library. We’ve covered the basic steps of importing the Pandas library, loading the CSV file, exploring the data, manipulating the data, and visualizing the data.
We hope that this tutorial has been helpful to you and that you’re now ready to start working with CSV files in Jupyter Notebook.
Importing “.csv” Files Using Jupyter Notebook
This will be my second blog post describing how to import CSV (Comma separated values) files into your desired platform to work with. My first post discusses how to import data into Google Colaboratory, a link to it can be found here. In this post I will be explaining methods for working specifically with Jupyter Notebook.
Both of the methods that I will discuss require pandas to work successfully, you can use the cell found below to import it. You will also want to have the file or files that you’re trying to work with to be in an easy to locate file path.
After you have imported pandas you will want to acquire the file path of your CSV file. Instructions on how to do this can be found here for Windows, and then for Macs. Once you have the file path copied you will want to use the code cell below to read in your file, it is important you include the “r” at the start of your file path.
This method is quite literally the same as the first, the only differences are that you don’t need to include the “r”. Once again to start, you will need pandas imported. After that you will still need to acquire your file path, after you have it copied and ready to paste, you will want to make sure to use double backslashes “ \\ ” as seen in the cell below.
Insightlist
3. Потом вызываем содержимое файла в таком виде с такими двумя слешами!
Второй способ открыть csv файл в Jupyter notebook уже без загрузки pandas выглядит так:
Если выгружать csv файл из Google-analytics там не только все в кучу, но еще и слетает кодировка.
Чтобы открыть файл одновременно избавившись от иероглифов в csv файле,
нужно прописать следующее
with open(‘file_name.csv’,’r’, encoding =’utf-8′) as f:
Для исправления кодировки указали этот кусок — encoding =’utf-8′
Получим открытый выбранный файл в нормальной кодировке.
Но не всегда! Бывает, что кодировка не восстанавливается.
3. Самый простой способ если файл лежит на вашем диске компа, а вы уже установили pandas, просто добавить путь «развернув» слеши. Вот так
Insightlist
3. Потом вызываем содержимое файла в таком виде с такими двумя слешами!
Второй способ открыть csv файл в Jupyter notebook уже без загрузки pandas выглядит так:
Если выгружать csv файл из Google-analytics там не только все в кучу, но еще и слетает кодировка.
Чтобы открыть файл одновременно избавившись от иероглифов в csv файле,
нужно прописать следующее
with open(‘file_name.csv’,’r’, encoding =’utf-8′) as f:
Для исправления кодировки указали этот кусок — encoding =’utf-8′
Получим открытый выбранный файл в нормальной кодировке.
Но не всегда! Бывает, что кодировка не восстанавливается.
3. Самый простой способ если файл лежит на вашем диске компа, а вы уже установили pandas, просто добавить путь «развернув» слеши. Вот так
Data Analysis: Loading Data from CSV & Excel Files in Jupyter Notebook
The first stage of data analysis is getting the data. Today with this tutorial we will see an easy and fast way to import our data in Jupyter Notebook.
To loading our CSV files or excel files, we will use Jupyter. If you don’t know what is it Jupyter, don’t worry; you can find some information about in my previous article click here Anaconda&Jupyter.
Thanks to Jupyter and Anaconda, with just a few lines of code, we will be able to import data
in the following formats:
- CSV
- Excel
- SQL (I will explain this in the next article)
Let’s see how to:
Import CSV Data:
Suppose to have a csv file with name data, you need to write this 4 code lines to get you data
Import Excel Data:
The method to import Excel data in Jupyter it’s really similar like as the method to import CSV data.
That’s all my friends !
In the next article we will see how to save data in CSV and Excel files, and how to combine Excel file.
How To Read CSV Files In a Jupyter Notebook Online
As a data scientist, one of the most common tasks you’ll encounter is reading data from CSV files. These files are widely used to store tabular data, and they can be easily created and manipulated using spreadsheet software like Microsoft Excel or Google Sheets. However, when working with large datasets, it’s often more convenient to use a programming language like Python and a tool like Jupyter Notebook. You can use Jupyter notebooks for free online at Saturn Cloud.
Jupyter Notebook is an open-source web application that allows you to create and share documents containing live code, equations, visualizations, and narrative text. It supports many programming languages, including Python, R, and Julia, and it’s widely used in data science, scientific research, and education.
Struggling with reading CSV files in Jupyter Notebook online? Simplify your data science tasks with Saturn Cloud. Begin your free trial today and experience seamless file handling!
In this tutorial, we’ll show you how to read a CSV file in Jupyter Notebook online using Python and the Pandas library. Pandas is a powerful data manipulation library that provides easy-to-use data structures and data analysis tools for Python.
Step 1: Import the Pandas library
To use the Pandas library, you need to import it into your Jupyter Notebook. You can do this by running the following command:
This command imports the Pandas library and assigns it the alias “pd”, which is a common convention in the Python community.
Step 2: Load the CSV file
To load a CSV file into Pandas, you can use the read_csv() function. This function takes the path to the CSV file as a parameter and returns a DataFrame object, which is a two-dimensional table-like data structure that can hold data of different types.
Assuming that your CSV file is stored in the same directory as your Jupyter Notebook, you can load it by running the following command:
This command reads the CSV file named “mydata.csv” and stores its contents in a DataFrame object named “df”. You can replace “mydata.csv” with the name of your CSV file.
If your CSV file is stored in a different directory, you need to provide the full path to the file. For example, if your CSV file is stored in the “data” directory of your Jupyter Notebook, you can load it by running the following command:
This command reads the CSV file named “mydata.csv” from the “data” directory and stores its contents in a DataFrame object named “df”.
Step 3: Explore the data
Once you’ve loaded the CSV file into a DataFrame object, you can start exploring its contents. Pandas provides many functions and methods for data manipulation, aggregation, and visualization.
For example, you can use the head() function to display the first five rows of the DataFrame:
This command displays the first five rows of the DataFrame. You can change the number of rows displayed by passing a parameter to the head() function. For example, to display the first ten rows, you can run:
You can also use the describe() function to get a statistical summary of the DataFrame:
This command displays the count, mean, standard deviation, minimum, and maximum values for each column of the DataFrame. If your DataFrame contains non-numeric columns, the describe() function will skip them.
Step 4: Manipulate the data
Pandas provides many functions and methods for manipulating the data in a DataFrame. For example, you can use the loc[] operator to select rows and columns based on their labels:
This command selects the first six rows of the DataFrame and the columns named “column1” and “column2”. You can replace “column1” and “column2” with the names of your columns.
You can also use the iloc[] operator to select rows and columns based on their positions:
This command selects the first six rows of the DataFrame and the first two columns.
Step 5: Visualize the data
Pandas provides many functions and methods for visualizing the data in a DataFrame. For example, you can use the plot() function to create a line plot of a column:
This command creates a line plot of the column named “column1”. You can replace “column1” with the name of your column.
You can also use the scatter() function to create a scatter plot of two columns:
This command creates a scatter plot of the columns named “column1” and “column2”. You can replace “column1” and “column2” with the names of your columns.
Struggling with reading CSV files in Jupyter Notebook online? Simplify your data science tasks with Saturn Cloud. Begin your free trial today and experience seamless file handling!
Conclusion
In this tutorial, we’ve shown you how to read a CSV file in Jupyter Notebook online using Python and the Pandas library. We’ve covered the basic steps of importing the Pandas library, loading the CSV file, exploring the data, manipulating the data, and visualizing the data.
We hope that this tutorial has been helpful to you and that you’re now ready to start working with CSV files in Jupyter Notebook.
How to Import a CSV into a Jupyter Notebook with Python and Pandas
Documentation for importing a CSV into a Jupyter Notebook with Python and Pandas
By David Allen on March 14th, 2022
If you're a spreadsheet ninja, I can only assume you'll want to start your Jupyter/Python/Pandas journey by importing a CSV into your Jupyter notebook.
Please enable JavaScript
Let me just say that this is very easy to do, and I'm excited to show you.
Hit that easy button and let's do it!
Table of Contents:
Do something to the CSV
Step 1: Getting started
First, you'll need to be set up with Python, Pandas, and Jupyter notebooks. If you aren't, please start here
Step 2: Imports
Next, you'll set up a notebook with the necessary imports:
Pandas is literally all you need for this operation, and it is often imported as pd. You'll use pd as a prefix for pandas operations.
This is what your notebook should look like:
Step 3: Read CSV
Next, you'll simply ask Pandas to read_csv, and then assign your spreadsheet a variable name. Sorta like this:
variable_name = pd.read_csv(‘file path')
The read_csv is a Pandas method that allows a user to create a Pandas Dataframe from a local CSV. You can read more about the operation here at https://pandas.pydata.org/, where you can find all the Pandas documentation you'll ever want.
Remember, we use the prefix pd to run any pandas operations:
But first, we'll need a CSV to read! Let's use something from kaggle.com. I think this Healthy Lifestyle Cities Report is interesting, so let's use that one.
If you don't have a Kaggle account, go ahead and register. It's a worthwhile site to know about. Loads of datasets to peruse.
Then, just hit the download button to grab all the project resources. Open the zip file and you'll find your CSV in your downloads folder (or where ever your downloads go). Make note of the location and filename.
Now, let's import that CSV!
You can use the tilda (
) and then a backslash(/) in front of “Desktop” or “Documents” or “Downloads” before hitting “tab” to get some autocomplete help with the file path.
It should look like this before you hit tab:
And then your computer should autocomplete the path for you, like this:
Then, just start typing out the file name and hit “tab” again to autofill the rest of the path.
See it in action:
Step 4: Do something to the CSV
Now that we've loaded our CSV into our notebook, it's time to do something with the CSV.
First, let's just take a look at the first 5 rows with a very popular command: head() .
This will show the first 5 rows (including column headers) of our DataFrame.
You can use the tab again to autocomplete the name of your variable spreadsheet
Just start typing spread and then hit tab.
Looks like this:
Very quickly, let's just sort the DataFrame by Sunshine hours(City), assign the sorted result to a new variable, and then we'll export this new CSV.
We'll assign the sorted DataFrame to a new variable df
.sort_values() does exactly what it sounds like. Just pass in the column name (or column names), and then specify whether or not you want to sort ascending or not. Setting ascending=False will sort the DataFrame in a descending manner.
Next, we'll complete the tutorial by exporting the sorted CSV.
Step 5: Export the CSV
Exporting is as simple as importing. Just use the pandas DataFrame method to_csv to save your df to local storage: