![]() ![]() > data = list(csv.DictReader(file, delimiter=","))Īs simple as that! Now, we can make extracting the grades prettier: Read about more details in the official documentation or learn in our CSV course. If you don't have a header row, you may specify the fieldnames argument. To do so, we use csv.DictReader().Īs the name suggests, it parses each row as a dictionary, using the header row to determine column names. Read a CSV File Into a List of DictionariesĪs promised earlier, we now see how to use column names from the header to access the data. And the third line is a basic division to get us the average. The second line calculates the length of the list, that is, the number of elements. > grades_avg = sum_grades / count_gradesĭone! Pretty self-explanatory, isn’t it? The first line sums the whole list. Let's assign that expression to the grades variable and calculate the two values we talked about at the start of this section. It's obvious we get the value from the third column, but we'll get back to this bit later in the article to use column names.Īhhh yes! That looks right. Side note: writing row is not the prettiest solution. Sounds mysterious? If we've managed to intrigue you, then check out Xavier's article, “ Map, Filter, and Reduce – Working on Streams in Python.” Then, we wrap row with the float() function to get numbers we can work with. This means we write data instead of simply data. To solve the first issue, we use index slicing and skip the first row. Second, we can’t calculate an average of strings, so we need to cast them to floats. First, we've left the header in the data. If you're not familiar with this syntax, Marija wrote an article about it check it out!īut our grades don't look right. We've used a construct called list comprehension here. Let's start by extracting grades from the data. Luckily, Python comes with functions for both of these tasks. To calculate the average manually, we need two values: the total sum of all grades and the count of grades. Note that you get an I/O error if you decide to stick with iterators and try to use them after closing. ![]() In the course, you also learn how to create and write your own CSV files.įinally, once we read the whole file and no longer need it, we can safely close it with file.close(). To become a pro at handling huge CSV files, check out our How to Read and Write CSV Files in Python course. If you ever work with enormous amounts of data – don't do this – you will probably run out of RAM in your device. To turn an iterable into a list, we wrap the whole expression with list(). To understand the bigger picture and become more fluent with iterables, take a look at the article “ An Introduction to Combinatoric Iterators in Python.” Think of it as a chain of data, accessed one by one and only once. When we pass a file handle, csv.reader() treats it as an iterable and reads the entire file line by line.Ĭsv.reader() also returns an iterable. As you may have gleaned from the name of the first argument, it expects to receive an iterable, so you can also pass a list of CSV rows (as text). We only need to specify the first argument, iterable, and we specify the comma as the delimiter. Next, we work on the opened file using csv.reader(). Then we open the file in the read mode and assign the file handle to the file variable. In the first line, we import the csv module. ,, ,, ,, ,, ,, ,, ,, ,, ,, ]īasically, that's it! Let's go through the script line by line. > data = list(csv.reader(file, delimiter=",")) Knowing all of that, let's write the code! ![]() We have the data in the format below, in a file called data.csv:Īs you see, it uses the comma as the separator, and we have a header row. For simplicity, let's assume grades range from 1 (worst) to 6 (best). You have names and grades, and you want to calculate the class average. Imagine you work with data from class exams. if you have reasons to rely on just pure Pythonic ways, here's how! Read a CSV File Into a List of Lists From there, you can go further with your data and visualize it.īut that’s not the only way. The easiest way to work with CSV files in Python is to use the pandas module. ![]()
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