From the course: Data Analysis with Python and Pandas
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The .loc accessor
From the course: Data Analysis with Python and Pandas
The .loc accessor
- [Instructor] Now that we've taken a look at the iloc method, it's time to look at loc. The loc method allows us to access data via its labels. I tend to find that I use this method a lot more than iloc, because a lot of the time I'm trying to access both rows as well as a subset of columns and referencing column names via other labels. So as opposed to referencing column index 012, I probably want to be accessing columns price, columns customer ID, column sales amount, that type of thing. So having this label based accessor makes it really nice. Additionally, if we do have a custom index, the loc method's going to be often more intuitive if we want to grab specific values with those custom indexes. So let's take a look. So once again, we're specifying DF here for data frame. This is just to let you know that this works on both data frames and series. We first pass in our row labels, so we could access a single row label. So for example, if our index had the word pizza in it, we…
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Contents
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Series basics10m
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pandas data types and type conversion6m 46s
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Challenge: Data types and type conversion2m 23s
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Solution: Data types and type conversion3m 5s
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The series index and custom indices7m 6s
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The .iloc accessor4m 33s
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The .loc accessor7m 3s
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Duplicate index values and resetting the index6m 33s
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Challenge: Accessing data and resetting the index2m 1s
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Solution: Accessing data and resetting the index2m 39s
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Filtering series and logical tests8m 19s
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Sorting series3m 45s
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Challenge: Sorting and filtering series57s
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Solution: Sorting and filtering series3m 24s
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Numeric series operations6m 31s
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Text series operations7m 4s
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Challenge: Series operations1m 36s
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Solution: Series operations3m 53s
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Numerical series aggregation5m 43s
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Categorical series aggregation3m 32s
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Challenge: Series aggregation50s
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Solution: Series aggregation4m 20s
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Missing data representation in pandas4m 29s
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Identifying missing data2m 15s
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Fixing missing data9m 27s
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Challenge: Missing data45s
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Solution: Missing data1m 35s
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Applying custom functions to series4m 6s
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pandas where() vs. NumPy where()6m 3s
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Challenge: apply() and where()1m 9s
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Solution: apply() and where()4m 37s
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Key takeaways1m 24s
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