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Column Information ​

When working with DataFrames, it's crucial to understand the structure and contents of your data. Before making any alterations to a DataFrame’s columns, you should familiarize yourself with the existing columns and the types of data they contain. This can be easily achieved using the .info() method.

Using .info() to Get Column Information ​

The .info() method provides a summary of your DataFrame's structure, including the number of entries, column names, data types, and non-null counts. Let's walk through an example using a DataFrame containing information about National Parks.

Example ​

Imagine you have a DataFrame called parks that contains data about various national parks. To get an overview of this DataFrame, you would use the following command:

python:
park.info()

The output from .info() starts with some general information about the DataFrame:

plaintext
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 72 entries, 0 to 71

The first line tells you that parks is a DataFrame (note that .info() can also be called on a Series). The second line indicates that the index of parks is a range with 72 entries, spanning from 0 to 71.

Next, .info() displays a table with detailed information for each column. For the parks DataFrame, this might look like:

plaintext
#    Column            Non-Null Count  Dtype
0    index             72 non-null     int64
1    Park              72 non-null     object
2    Location          72 non-null     object
3    AnnualPassPrice   72 non-null     int64

Here's what each column represents:

  • #: The column index number.
  • Column: The name of the column.
  • Non-Null Count: The number of non-missing (non-null) values in the column.
  • Dtype: The data type of the column.

It's important to note that the index in the table is a column named index within the DataFrame, and not the DataFrame's RangeIndex, which was described above the table.

Interpreting the Information ​

From the output, you can deduce that:

  • Each column has the expected data type.
  • There are no missing values, as indicated by the non-null counts matching the number of entries (72).

After the table of column information, .info() provides a summary of the number of columns by data type:

plaintext
dtypes: float64(1), int64(4), object(4)

This summary tells you how many columns of each data type exist in the DataFrame. In this case, there is one float64 column, four int64 columns, and four object columns.

Finally, .info() prints the amount of memory being used by the DataFrame:

plaintext
memory usage: 5.2+ KB

Conclusion ​

Using the .info() method is a simple yet powerful way to get a quick overview of your DataFrame's structure and contents. By understanding the column information, you can ensure that your data is in the expected format and identify any potential issues before performing further analysis or modifications. This foundational step is essential for maintaining the integrity and quality of your data work.