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There is a general idea of how to generate a DataFrame. I am telling you one by one and giving you examples so that you are clear about it. The table of contents can be used to find the one you need or the one you are interested in.

If you subscribe to the Python Code mailing list, you will get a free Python Cheat Sheet to speed up your learning, along with programming tips, tricks, and news.

It's important to make sure you understand what data format it is before you load it in a DataFrame.

A DataFrame is a two-dimensional data table with rows and columns. Each row and column can have a name or label.

For example, we can save our class schedule in a DataFrame, where the columns are the days, the rows are the hours, and the values are each class or subject. We could save the flight departures where the columns represent the flight number, departure time and destination.

If you find data in a table that has a single column or a single row, it is valid to load it in a DataFrame.

One row and one column is possible.

I give you an example of a table that can be used to visually contrast what I am telling you.

In some of the examples in this article, we will use a table like this one.

The first situation in which we can find ourselves is that we have to create a DataFrame because we don't have the data.

We want to save the data in a DataFrame when we execute a process that is gradually generating it.

An empty DataFrame is the best option. We can add data to it so it grows quickly.

The first thing to know is that our DataFrame will be an instance of the DataFrame class from the pandas library.

We are going to use the most direct way to create an object that is using its constructor.

Make sure the library is installed. You can use the pandas command.

In this case, we can call the constructor without parameters and have our DataFrame ready to accept data.

To be able to work with the library, it is necessary to import it. We shorten the code by changing it to pd.

Since there cannot be a table without columns, we have to define some columns.

We see it.

Row access is more direct than column access, because the DataFrame's primary dimensions are columns.

We access the columns before the rows using the typical brackets notation.

One way to add a new column to a DataFrame is to assign the values that column should have, just as we would do in a dictionary and brackets.

I indicate None since we don't want to introduce values.

The result of the above code is that the DataFrame has a new column named Name. For now we can ignore the other piece of information called Index.

The assign function of DataFrame can be used to add columns.

The function allows us to add columns to the ones already created. It does not add them to the original DataFrame, but it does return a new one with the new columns and the original columns.

The result of running the above code

We already know how to add columns, but... you agree that a DataFrame without data is not very useful? Let's see how to add data.

We can only add data to the DataFrame once it is already created. There are ways to do it.

Let's see a few of them.

Suppose we have the data to make a list for each column with the values of each row for that column. We can make a simple assignment

This operation will result in the following.

Since the assignment already creates the column if it doesn't exist, we don't need to create the columns again. If the column already exists, you may be pounding old values with the new ones.

We can add new values by putting entire rows in. It helps us to be able to insert values little by little as we receive them. The DataFrame objects can be used to add a row to the end of the table.

A pandas Series object can be used to represent a list of values or a dictionary object, where each value has a key to the name of the column in the table. In the same example, let's see the forms.

There are several things to notice.

The result is:

The same thing happens with the rows as the columns of a DataFrame can. Each row has a name or label. Imagine a weekly calendar where each row represents a day of the week. We can label each row with the names Monday, Tuesday, Wednesday.

It is very convenient to be able to access specific rows without knowing their position in the table. We are going to create a DataFrame that will store the medication that a person has to take in the morning, afternoon and night.

The loc attribute of a DataFrame is used to add data. Let's look at an example.

The form has an advantage that we don't need to indicate the column names. It is necessary to give the values in the correct order.

The result is as follows

On Tuesday afternoons there is no medication. I can tell you the value in that case.

When the DataFrame is empty, these label or row names are the set of indices that are supposed to be displayed on the screen.

There are many ways to insert data, but we want to see the different ways to create a DataFrame.

We've seen how to create an empty one and how to create another one from column values, so let's move on to other ways.

If we want to learn pandas, we need to create an empty DataFrame first, and then create a list of lists from it, which can also be viewed as an array. Check out this article on getting a column from an array in Python for more information on handling lists of lists.

To create a DataFrame from a list of lists, it is necessary to call the constructor and provide the data list as aParameter. The DataFrame object will be returned when the call is made.

Suppose you have a list of three lists with four values each that represent a different part of the data table.

We want to create a DataFrame from the lists. We can do this.

By default, the column names are 0, 1, 2, and 3, and the row names are 0, 1, and 2.

The rows in the DataFrame correspond to the rows in the original lists.

If what you want is that each row of your list of lists becomes a column in the DataFrame you will have to transpose the DataFrame, that is, exchange rows for columns, when creating it using the transpose function in this way df = pd .DataFrame(data).transpose().

If you need custom column names, you can add the columns parameters to the call, indicating their names in a list.

If you want to give names to the rows, you can do it with the index parameter.

The result will be the following.

A common case is having the data for each column of the desired table in a dictionary.

To create a DataFrame from a dictionary of lists, you need to supply the dictionary to the DataFrame class.

This call returns a DataFrame object with the data from the dictionary in column names.

Suppose you have a dictionary with three lists that are stored by the keys Name, Age and Department. The DataFrame's constructor requires that dictionary to be provided.

And ready!

We can get the following result easily.

In the previous case, we had the data stored by columns in a dictionary of lists, but in this case, we may have it stored by rows in a list of dictionaries. Sounds like a tongue twister.

To create a DataFrame from a list of dictionaries, you need to provide the list to the DataFrame class.

This call returns a DataFrame object with the data from the list.

The example is similar to this one:

The result is:

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