Python for Data Science For Dummies Cheat Sheet

Every developer on the planet makes mistakes. However, knowing about common mistakes will save you time and effort later. The following list tells you about the most common errors that developers experience when working with Python:

• Using the incorrect indentation: Many Python features rely on indentation. For example, when you create a new class, everything in that class is indented under the class declaration. The same is true for decision, loop, and other structural statements. If you find that your code is executing a task when it really shouldn’t be, start reviewing the indentation you’re using.

• Relying on the assignment operator instead of the equality operator: When performing a comparison between two objects or value, you just use the equality operator (==), not the assignment operator (=). The assignment operator places an object or value within a variable and doesn’t compare anything.

• Placing function calls in the wrong order when creating complex statements: Python always executes functions from left to right. So the statement MyString.strip().center(21, "*") produces a different result than MyString.center(21, "*").strip(). When you encounter a situation in which the output of a series of concatenated functions is different from what you expected, you need to check function order to ensure that each function is in the correct place.

• Misplacing punctuation: You can put punctuation in the wrong place and create an entirely different result. Remember that you must include a colon at the end of each structural statement. In addition, the placement of parentheses is critical. For example, (1 + 2) * (3 + 4), 1 + ((2 * 3) + 4), and 1 + (2 * (3 + 4)) all produce different results.

• Using the incorrect logical operator: Most of the operators don’t present developers with problems, but the logical operators do. Remember to use and to determine when both operands must be True and or when either of the operands can be True.

• Creating count-by-one errors on loops: Remember that a loop doesn’t count the last number you specify in a range. So, if you specify the range [1:11], you actually get output for values between 1 and 10.

• Using the wrong capitalization: Python is case sensitive, so MyVar is different from myvar and MYVAR. Always check capitalization when you find that you can’t access a value you expected to access.

• Making a spelling mistake: Even seasoned developers suffer from spelling errors at times. Ensuring that you use a common approach to naming variables, classes, and functions does help. However, even a consistent naming scheme won’t always prevent you from typing MyVer when you meant to type MyVar.

Whenever you create a plot, you need to identify the sources of information using more than just the lines. Creating a plot that uses differing line types and data point symbols makes the plot much easier for other people to use. The following table lists the line plot styles.

Remember that you can also use these styles with other kinds of plots. For example, a scatter plot can use these styles to define each of the data points. When in doubt, try the styles to see whether they’ll work with your particular plot.

It’s kind of amazing to think that IPython provides you with magic, but that’s precisely what you get with the magic functions. A magic function begins with either a % or %% sign. Those with a % sign work within the environment, and those with a %% sign work at the cell level.

Note that the magic functions work best with Jupyter Notebook. People using alternatives, such as Google Colab, may find that some magic functions fail to provide the desired result.

The following list gives you a few of the most common magic functions and their purpose. To obtain a full list, type %quickref and press Enter in the IPython console or check out the full list.

Scikit-learn is a focal point for data science work with Python, so it pays to know which methods you need most. The following table provides a brief overview of the most important methods used for data analysis.