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![Indexing, Selection and Filtering
■ Series indexing (obj[...]) works analogously to NumPy array
indexing, except you can use the Series’s index values
instead of only integers
■ Slicing with labels behaves differently than normal Python
slicing in that the end point is inclusive
‐
■ Setting using these methods modifies the corresponding
section of the Series
■ Indexing into a DataFrame is for retrieving one or more
columns either with a single value or sequence](https://image.slidesharecdn.com/pythonlecture9-251109165114-cd9f82f2/75/Python_Lecture9_DataStuctureswithPandas-pptx-14-2048.jpg)
Python_Lecture9_DataStuctureswithPandas.pptx
- 1. LECTURE 9 DATA STRUCTURES ANDANALYSIS WITH PANDAS
- 2. Thus, whenever yousee pd. in code, it’s referring to pandas. You may also find it easier to import Series and DataFrame into the local namespace since they are so frequently used:
- 3. Introduction to pandasData Structures Series. ■ A Series is a one- dimensional array-like object containing a sequence of values (of similar types to NumPy types) and an associated array of data labels, called its index. The simplest Series is formed from only an array of data:
- 4. ■ Using NumPyfunctions or NumPy-like operations, such as filtering with a boolean array, scalar multiplication, or applying math functions, will preserve the index-value link:
- 5. ■ Another wayto think about a Series is as a fixed-length, ordered dict, as it is a map ping of index values to data values. It can be used in many contexts ‐ where you might use a dict: ■ Should you have data contained in a Python dict, you can create a Series from it by passing the dict:
- 6. Data Frame ■ ADataFrame represents a rectangular table of data and contains an ordered collec tion of columns, each of which ‐ can be a different value type (numeric, string, boolean, etc.).
- 7. Data Frame ■ Whenyou are assigning lists or arrays to a column, the value’s length must match the length of the DataFrame. If you assign a Series, its labels will be realigned exactly to the DataFrame’s index, inserting missing values in any holes:
- 8. Possible data inputsto Data Frame constructor
- 9. Index Objects ■ pandas’sIndex objects are responsible for holding the axis labels and other metadata (like the axis name or names). Any array or other sequence of labels you use when constructing a Series or DataFrame is internally converted to an Index:
- 10. Index methods andproperties
- 11. Reindexing ■ An importantmethod on pandas objects is reindex, which means to create a new object with the data conformed to a new index.
- 12.
- 13. Dropping Entries froman Axis ■ Dropping one or more entries from an axis is easy if you already have an index array or list without those entries. As that can require a bit of munging and set logic, the drop method will return a new object with the indicated value or values deleted from an axis:
- 14. Indexing, Selection andFiltering ■ Series indexing (obj[...]) works analogously to NumPy array indexing, except you can use the Series’s index values instead of only integers ■ Slicing with labels behaves differently than normal Python slicing in that the end point is inclusive ‐ ■ Setting using these methods modifies the corresponding section of the Series ■ Indexing into a DataFrame is for retrieving one or more columns either with a single value or sequence
- 15. Selection with locand iloc ■ For DataFrame label-indexing on the rows, the special indexing operators loc and iloc are introduced. They enable you to select a subset of the rows and columns from a DataFrame with NumPy-like notation using either axis labels (loc) or integers (iloc).
- 16. Indexing options withDataFrame
- 17. Arithmetic and DataAlignment ■ An important pandas feature for some applications is the behavior of arithmetic between objects with different indexes. When you are adding together objects, if any index pairs are not the same, the respective index in the result will be the union of the index pairs. This is similar to an automatic outer join on the index labels ■ The internal data alignment introduces missing values in the label locations that don’t overlap. Missing values will then propagate in further arithmetic computations.
- 18.
- 19. Function Application andMapping ■ NumPy ufuncs (element-wise array methods) also work with pandas objects ■ Another frequent operation is applying a function on one-dimensional arrays to each column or row. DataFrame’s apply method does exactly this ■ Many of the most common array statistics (like sum and mean) are DataFrame methods, so using apply is not necessary.
- 20. Sorting and Ranking ■Sorting a dataset by some criterion is another important built-in operation. To sort lexicographically by row or column index, use the sort_index method, which returns a new, sorted object:
- 21. ■ Ranking assignsranks from one through the number of valid data points in an array. The rank methods for Series and DataFrame are the place to look; by default rank breaks ties by assigning each group the mean rank:
- 22.
- 23. Axis Indexes withDuplicate Labels
- 24. Summarizing and Computing DescriptiveStatistics
- 25. Descriptive and summarystatistics
- 26. Unique Values, ValueCounts and Membership