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Data Wrangling with Python by Katharine Jarmul, Jacqueline Kazil

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Chapter 9. Data Exploration and Analysis

Now that you’ve spent time acquiring and cleaning your data, you are ready to start analyzing! It’s important to approach your data exploration with very few expectations for outcomes. Your question could be too broad for a singular answer, or it might not have a conclusive answer. Recall learning about hypotheses and conclusions in your first science course? It’s best to approach your data exploration with those same methods in mind—and with an understanding that you may not find a clear conclusion.

That said, just exploring the data and finding there are no trends or the trends don’t match your expectations is part of the fun. If everything was how we expected it to be, data wrangling would be a bit boring. We’ve learned to expect little and explore a lot.

Note

As you begin to analyze and explore your data, you might realize you need more data or different data. That’s all part of the process and should be embraced as you further define the questions you aim to answer and examine what the data is telling you.

Now is also a great time to revisit the initial questions you had when you found your dataset(s). What do you want to know? Are there other related questions to aid your exploration? Those questions might point you in a direction where you find a story. If not, they might lead to other interesting questions. Even if you can’t answer your initial question, you can reach a greater understanding of the topic and discover new questions ...

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