To open a big CSV with Row Zero, click Data > Upload File and select your CSV. The file will upload to the spreadsheet where you can write functions, pivot, and graph in a blazingy fast spreadsheet that supports millions of rows data.
As datasets have grown, routine data analysis often requires opening and working with big CSV files, which can be challenging due to their size. The following is an overview of challenges opening and working with big CSVs and the top 5 software applications that can open a large CSV.
What does 'CSV' stand for and what are CSVs?
CSV stands for comma separated values. The CSV file format is a file that contains text separated by commas as shown in the example below.
,Date,Team,Acquired,Relinquished,Notes 1,1961-11-26,Lakers,, Elgin Baylor,player reported for military duty 2,1962-03-24,Lakers, Elgin Baylor,,player given 2-day pass from military duty 3,1962-03-31,Lakers, Elgin Baylor,,player given weekend pass from military duty 4,1965-09-11,Pistons,, Terry Dischinger,service in army (date approximate) 5,1969-10-15,Bucks,, Bob Greacen,placed on IL with sprained ankle 6,1969-10-28,Bucks, Bob Greacen,,activated from IL 10,1969-12-03,Bucks,, John Arthurs,deployed to National Guard for remainder of the season 7,1970-01-24,Bucks,, Guy Rodgers,placed on IL with torn leg muscle
CSV files are common file formats because the simple schema makes them easy to process and they can be opened by a large number of software products. The simplicity of the file format, which is simply text separated by commas, makes CSVs an attractive choice for storing any dataset but particularly well suited for large datasets. Challenges arise when an application opens a big csv and needs to edit or transform it, which can require a lot of processing and compute resources.
Challenges Working with a Large CSV file
The most common choices for opening csv files are popular spreadsheet programs, like Excel and Google Sheets; however, those applications have dataset size and row limits that make it difficult to open large files. Unlike Row Zero, spreadsheet programs were not not design to work with big data. You can read about how to handle large datasets with those sreadsheet programs in our blog posts
- How to open a csv file too large for Excel
- How to open a csv too large for Google Sheets
- How to open a csv too large for Apple Numbers
Aside from the dataset size, it is also important to consider what needs to be done with the big csv file once it is opened. Below we outlined the 3 common use cases to consider when selecting which tool to open a large CSV file.
- Exploring - Once you open a big CSV file it is common to scroll through the dataset to understand what data are in each column and the format of the data.
- Manipulating - Big CSVs aren't just for looking, they need to be sorted, filtered, pivoted, or transformed to view and extract the relevant information.
- Joining - CSVs are not always stand-alone files. They regularly need to be joined with another dataset in order to perform an analysis.
Top 5 Tools For Opening Big CSVs
Below are instructions to open a big CSV file in the following programs:
1. Row Zero
To open a big csv simply click data, select 'upload file' and navigate to the big CSV file you wish to open. Select the file and click 'Open.' Row Zero will open the file in a matter of seconds and import it into the spreadsheet. Row Zero is a blazingly fast spreadsheet that is designed to handle large datasets without crashing. The application runs in the cloud and makes use of the larger compute resources than what is typically available on an average computer. Row Zero is 100 times faster than Microsoft Excel and big CSVs can easily be uploaded from a local computer or imported directly from data repositories, like AWS S3. Once the large CSV is imported into Row Zero, the spreadsheet can easily support many millions of rows with sorting, filtering, pivot tables, and all the spreadsheet functions that are found in Excel and Google Sheets. Click here for instructions on editing and exploring big csvs.
Step by step instructions are listed below:
- Click on the 'Data' menu
- Select upload from computer or import from S3, using the import from link or Connect a S3 Bucket methods.
- Review import details and click 'Import.'
*Note - If your csv is a download or extract from a data warehouse, you can connect Row Zero directly to your data warehouse (e.g. Snowflake, Redshift, Postgres, and others.) in a few easy steps. By connecting directly to a data warehouse, you can build a model or analysis that is refreshable when new data reaches the database. To view all data import options check out our data import documentation.
Once data is imported, Row Zero supports all the normal spreadsheet features, like formatting, sorting, filtering, joining, analyzing, and any other manipulation you can dream up. Row Zero is free and a new workbook can be opened by visiting https://rowzero.io/new.
Microsoft Excel on its own can only handle csvs up to the 1,048,576 row limit as it was not designed to work with big data. However, there are features, like Data Model, Power Query and Power Pivot that support opening large CSV files, sorting, filtering, and pivoting. The features are not easy to find within Excel. Follow instructions below to upen CSV files bigger than the excel row limit.
Click the ‘Data’ menu across the top, then select ‘Get Data.’
Then select the data type you would like to import and click ‘import.’
Once the preview launches, select ‘Load to’ from the bottom drop down.
You will see several options
- Pivot table - Allows interactions with the data in a pivot table format.
- Pivot chart - Allows pivot interactions with the data, which generates a chart.
- Table - Loads data to a worksheet. Only the first 1,048,576 rows.
- Only Create Connection - Creates a connection to the data set. Data can be viewed by clicking on ‘Manage data Model’ or double clicking the data connection.
Make sure in each case you select ‘Add this data to the Data Model,’ otherwise the workbook won’t support more rows than the row limit.
Jupyter is a tool best suited for software engineers, data scientists, and analysts that know how to code. The application enables manipulation of large data sets with commands written in code cells and can handle millions of rows, though it runs locally and is dependent on the hardware in a user’s computer. To install Jupyter Notebook on your computer, follow these install instructions. Then follow the instruction below.
- Launch Jupyter Notebook
- Create a new notebook by clicking on the "New" button and selecting "Python 3" or any other suitable kernel you prefer.
- Import pandas library and read the CSV file. In a new code cell, import the pandas library by typing the following code and running the cell:
import pandas as pd
- Specify the path to your CSV file by providing the file's location in your local file system. If the file is in the same directory as your Jupyter Notebook file, you can simply provide the filename.
- Use the read_csv() function from pandas to read the CSV file. Here's an example of how to read a CSV file named "data.csv":
df = pd.read_csv('data.csv')
- If your CSV file has a different delimiter (e.g., a tab-separated file), you can specify it using the delimiter parameter in the read_csv() function. For instance, to read a tab-separated file, you can use:
df = pd.read_csv('data.csv', delimiter='\t')
- If your CSV file has a large number of columns and you only need to preview the data, you can use the nrows parameter to limit the number of rows read. For example, to read the first 1000 rows of the CSV file, you can use:
df = pd.read_csv('data.csv', nrows=1000)
After running the code cell, Jupyter Notebook will read the CSV file and store it in a pandas DataFrame named df. You can use this DataFrame to manipulate and analyze the data further.
SQLite is an easy to install lightweight SQL database that can be installed and run on a user’s computer using command line prompts. It can store millions of rows of data and easily access them with SQL database queries using the command line or another SQL editor. SQLite database is commonly used by many other software applications and because of this there is a large amount of documentation and helpful content across the web that makes it easy to resolve issues if you get stuck. In order to use the SQLite SQL Database to open large CSV files, follow the steps below.
- Download and run SQLite using the command line.
- Use command line prompts to create a databse, create a table, and specify the column names and types.
- Import a giant csv using the '.import' command line prompt.
- Write a SQL query to view the first x number of rows of the csv file.
5. Notepad ++
Notepad++ is a text editor that can easily open big CSV files and display the content in plain text format. The plain text format can be difficult to read because columns aren't always aligned. It is also difficult to make column level edits and transformations unless you know a programming language. Notepad++ is free to download and is easily installed. Once installed on a computer, open the program and select File>Open to open a big CSV file. Once open you can scroll through the data and make simple transformations.
Working with big CSV files can be tricky but there are some great tools to open, edit, transform, and analyze giant CSVs without having to know a programming language. We covered the challenges working with large CSVs and reviewed Row Zero, Excel, Jupyter Notebook, SQLite, and Notepad++. Row Zero is the fastest application for opening and analyzing a large CSV file. Excel can be great if it is not necessary to see every row and prefer some of the features it offers. Jupyter Notebooks are best suited for people who can write code. SQLite is great for SQL lovers and Notepad++ is easy to use for simple formatting and exploring.