The most common object in the pandas library is, by far, the dataframe object. Methods and Attributes of the DataFrame Structure This is just so we can rename the column headers while reading the data into memory. You may have noticed we assigned a list of strings to the names parameter in the read_csv function. Iris_data = pd.read_csv(url, names=col_names) We will use the Iris dataset from the UCI repository as an example: # Webpage URL Let’s say there’s data you want from a specific webpage how would you read it into memory? It’s ultimately the same process, except that you’re no longer passing a file path. Once you know how to read a CSV file from local storage into memory, reading data from other sources is a breeze. If that’s a little too easy, there is also the intermediate importing data in Python interactive course. We recommend you bookmark the importing data in Python cheat sheet and check out Introduction to importing data in Python to learn more. We have barely scratched the surface of different ways to customize the output of the read_csv function, but going into more depth would certainly be an information overload. Usecols = Īirbnb_data = pd.read_csv('data/listings_austin.csv', index_col='id', usecols=usecols) If you do know the columns you need, you’re in luck you can save time and memory by passing a list-like object to the usecols parameter of the read_csv function. Using the read_csv function, you can select only the columns you need after loading the file, but this means you must know what columns you need prior to loading in the data if you wish to perform this operation from within the read_csv function. What if you only want to read specific columns into memory because not all of them are important? This is a common scenario that occurs in the real world. Selecting specific columns to read into memory # airbnb_data = pd.read_csv('data/listings_austing.csv', index_col=0) # Setting the id column as the indexĪirbnb_data = pd.read_csv('data/listings_austin.csv', index_col='id') Let’s read in the data again and set the id column as the index.

Assigning the parameter a sequence will result in a multiIndex (a grouping of data by multiple levels). Note the value you assign to index_col may be given as either a string name, column index or a sequence of string names or column indexes. However, you can explicitly specify what column to make as the index to the read_csv function by setting the index_col parameter. The default behavior of pandas is to add an initial index to the dataframe returned from the CSV file it has loaded into memory. Printed the first five rows of the dataframe.īut there’s a lot more to the read_csv() function.

Passed the filepath to read_csv to read the data into memory as a pandas dataframe.Imported the pandas library into our environment.import pandas as pdĪirbnb_data = pd.read_csv('data/listings_austin.csv')Īll that has gone on in the code above is we have: Then we can talk about what’s going on and how we can customize the output we receive while reading the data into memory. Let’s write some code to import a file using read_csv(). Thus, it’s recommended you skim the file before attempting to load it into memory: this will give you more insight into what columns are required and which ones can be discarded. Importing a CSV file using the read_csv() functionīefore reading a CSV file into a pandas dataframe, you should have some insight into what the data contains. Note: Check out this DataLab workbook to follow along with the code. Practice pandas functions with hands-on exercises from our Intermediate Python course. We will also cover the different methods available to a pandas dataframe object, including how to write pandas dataframe to a CSV file and how to quickly learn more about your data through various methods and attributes. In this article, you will learn about the read_csv() function and how you can alter the parameters to customize the output you receive once the function is executed. For example, you can use pandas to perform merging, reshaping, joining, and concatenation operations. For data available in a tabular format and stored as a CSV file, you can use pandas to read it into memory using the read_csv() function, which returns a pandas dataframe. One of the most important functionalities of pandas is the tools it provides for reading and writing data.

Pandas is a widely-used Python library for data science, analysis, and machine learning that offers a flexible and intuitive way to handle data sets of all sizes. Read the Spanish version 🇪🇸 of this article.

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