Analyzing Data Using Python: Cleaning & Analyzing Data in Pandas
Python 3.8
| Intermediate
- 13 Videos | 1h 53m 17s
- Includes Assessment
- Earns a Badge
For data analysis to be useful and accurate, the analyzed data needs to be cleaned and curated. There are copious methods to achieve this in pandas. In this course, you'll learn how to identify and eliminate duplicates in pandas. You'll start by using the pandas cut method to discretize data into bins, using bins to plot histograms and identify outliers using box-and-whisker plots. You'll parse and work with datetime objects read in from strings and convert string columns to datetime using the dateutils python library. Moving on, you'll master different pandas methods for aggregating data - including the groupby, pivot, and pivot_table methods. Lastly, you'll perform various joins - inner, left outer, right outer, and full outer - using both the merge and join methods.
WHAT YOU WILL LEARN
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discover the key concepts covered in this courseidentify and deal with duplicate recordssummarize records into bins or categoriescompute aggregations on dataperform common grouping and aggregation operationsuse pivot tables to explore datause pivot tables to summarize data
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combine and merge recordsperform inner join operations using the merge() methodperform left and right join operations using the merge() methodimplement joins using the join() methodmanipulate and analyze time series datasummarize the key concepts covered in this course
IN THIS COURSE
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1.Course Overview3m 17sUP NEXT
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2.Identifying Duplicates in Data6m 29s
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3.Categorizing and Binning Data6m 48s
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4.Computing Aggregations on Data10m 42s
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5.Grouping and Aggregating Data12m 54s
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6.Viewing Data Using Pivot Tables9m 50s
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7.Summarizing Data Using Pivot Tables13m 10s
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8.Combining Data in DataFrames10m 19s
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9.Implementing Inner Joins8m 32s
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10.Implementing Left and Right Joins7m 50s
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11.Performing Joins Using DataFrame Indexes7m 20s
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12.Analyzing Time Series Data13m 25s
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13.Course Summary2m 41s
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