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608-mod5

Chapter 7 Notes:

NumPy:

• First appeared in 2006 and is the preferred python array implementation.
• Offers, ndarray which from this point forward we'll refer to by its synonym array.

Creating arrays from exisiting data:

• Import numpy module as np.
• array function recieves as an argument an array or other collection of elements and returns a new array containing the arguments elements.
• You can also create multidimensional arguments by using multiple [] []

Array Attributes:

• An array provides attributes that enable you to discover information about its structure and contents.
• You can check the element type with an array's dtype attribute.
• The attribute ndim contains an array's number of dimensions and the attribute shape contains a tuple specifying an array's dimensions.
• You can view an array's total elements using size and number of bytes required to store each element with itemsize.
• You can iterate through a multidimensional array as if it were one-dimensional by using its flat attribute.
• By default NumPy does not display trailing zeros in the fractional part of a floating point value.

Filling array with specific values:

• NumPy provides functions zeros, ones, and full for creating arrays containing 0s 1s, or a specified value.
• np.zeros(5) populates an array with 5 zeros, np.ones((2,4), dtype=int) creates a 2x4 array with ones, full is the same just specify value.

Creating arrays from Ranges

• NumPy's arrange function creates integer ranges.
• np.arrange(5) will spit out array([0,1,2,3,4])
• The linspace function produces evenly spaces floating point ranges.
• You can also reshape an array using reshape to transform a one-dimensional array into a multidimensional array.
• When displaying over 1000 items or more NumPy will "hide" values.

List versus array performance: Introducing %timeit

• %timeit magic command times the average duration of operations.

• After using the function it will display the average execution time.

• Arrays typically run faster than lists.

• -n and -r all you to customize the loop and number of loops.

• Other iPython magics

  • %load to read coad into IPython from a local file or URL
  • %save to save snippets to a file.
  • %run to execute a .py file from IPython
  • %precision to change the default floating-point precision for Ipython outputs.
  • %cd to change directories without having to exit IPython first
  • %edit to launch an external editor
  • %history to view a list of all snippets and commands you've executed in the current IPython session.

  Array Operators:

  • You can use arithmetic operators to calculate on an array.
  • The original array will stay the same, simply the outputs will change based on arithmetic.
  • When multiplying by a single value (scalar) NumPy populates an array of the same size to multiple by, this is called broadcasting.
  • You can also perform arithmetic between arrays of the same shape.
  • You can also do comparative statements on arrays and will evaluate on an element level.

  NumPy Calculation Methods:

  • You can also use various methods to perform calculations on an array like mean, median, mode, var, std.
  • You can also perform calculations on specific array dimensions known as the array's axes.
  • Specifying axis = 0 performs on all the row values within each column.

  Universal Functions:

  • There are universal functions that performs various elemen-wise operations.
  • sqrt calculates square root of values.
  • Use add to add two arrays with the same shape.
  • Multiply is also a universal function.

  Indexing and Slicing:

  • You index multiple arrays list you would a list. With the [] formate.
  • To select a single row, specify only one index in square brackets.
  • To select multiple sequential rows, use the slice notation --> [0:2] to select multiple rows.
  • To select multiple non-sequential rows, use a list of row indices.
  • You can select subsets of the columns by providing a tuple specifying the rows and columns to select.
  • :,0 selects column 0, the : indicates which rows within the column to select which represents all rows, if a number followed this would be the row number.

  Views: Shallow Copies:

  • View objects are objects that see the data in other objects, rather than having their own copies of the data.
  • Views are also known as shallow copies.
  • The array method view returns a new array object with a view of the original array object's data.
  • Changing the view also changes the value of the original array.
  • You can also use a slice [:] to create a view.
  • Trying to access elements in a view creates an IndexError.

  Deep Copies:

  • A deep copy has independent copies of the original data.
  • The array copy method returns a new array object with a deep copy of the orginial array object's data.
  • When doing this, modifying the original doesn't change the copy.
  • Other types of python objects use deepcopy function.

  Reshaping and Transposing:

  • reshape returns a view of the original array with the new dimensions it does not modify the original arary.
  • resize modifies the original arrays shape. You cab take multidimensional arrays and use flatten to make it single dimensional with a deep copy.
  • ravel produces a view of the original array
  • You can quickly transpose an array's rows and columns -- that is "flip" the array, so the rows become the columns and columnes become rows. Use .T to do this and produce a shallow copy and does not modify original array.
  • You can combine arrays by adding more columns or more rows -- known as horizontal and vertical stacking.
  • Combine two arrays using hstack to combine horizontalls or vstack to combine vertically.

  Pandas series:

  • A series is an enhances one-dimensional array.
  • Series support custom indexing, including even non-integer indices like strings.
  • The default indice starts at 0.
  • Pandas displays a series in a two-column format.
  • Can use mean, count, min, max, std, and describe.
  • The describe method produces all stats.
  • The interquartile range is the 75% minus 25% quartile.
  • You can also create a series with custom indices.
  • You can initialize a series with a dictionary, its keys become the series' indices and its values become the elements.

  DataFrames:

  • A dataframe is an enhanced two-dimensional array.
  • Can change the indices on a dataframe.
  • The loc attribute allows you to access a row by its label. Can also use iloc
  • You can also use an index as a slice.
  • Can select subsets of rows and columns.
  • Compatible with boolean indexing where you can look for specific values, ie all grades over 90.
  • Grades that don't satisfy the condition are represented as NaN (Not a Number).
  • Can use at and iat to get a single value from a DataFrame.
  • Can use describe on data frames as well.
  • Can Transpose use .T
  • sort_index allows you to sort rows in descending order.
  • axis=1 keyword argument indicates that we wish to sort the column indices.

  Chapter 8 Notes:

  Formatting Strings:

  • Presentation Types
    • Have worked with presentation type f in the format specifier.
    • d presentation type formats integer values as strings
    • c presentation type formats an integer character code as the corresponding character
    • s presentation type is the default.
    • Exponential notation can be used to format the values more compactly.
  • Field Widths and Alignment
    • Previously, used field widths to format text in a specified number of character options.
      • By default, python right aligns numbers and left aligns other values like strings.
      • Specify alignment using < and > for left and right alignment.
    • You can center values using ^
  • Numeric Formatting
    • You can use a + to force a positive sign on a number, similarly with a -.
    • You can format numbers with thousands separators by using a comma.
  • String's Format Method
    • Prior to f-strings, the format function was used.
    • Can contain multiple placeholders
    • Can reference arguments by position number.

Concatenating and Repeating Strings:

• You can perform concatenations using augmented assignments.
• Combine two strings using += Stripping Whitespace from strings
• Use method .strip to remove the leading and trailing whitespace.
• lstrip removes only leading whitespace.

Changing Character Case:

• Method capitalize copies the original string and returns a new string with only the first letter capitalized.
• Method title copies the original string and returns a new string with only the first character of each word capitalized.

Comparison Operators for Strings:

• Use comparison operators to compare strings - these are case sensitive.

Searching for Substrings:

• count for a substring returns the number of times its argument occurs in the string.
• If you specify a start and end index, will only search up to ending argument.
• index searches for a substring within a string and returns the first index at which the substring is found.
• rindex performs the same operation as index, but searches from the end of the string and returns the last index at which the substring is found.
• find and rfind perform the same tasks as index and rindex but, if the substring is not found, return -1 rather than causing a ValueError.
• Can use in to ask if a substring is in a string.
• startswith and endswith return TRUE if the string starts with or ends with a specified substring. Replacing substrings:
• Method replace takes two substrings. It searches a string for the substring in its first argument and replaces each occurence with the substring in its second argument.

Splitting and Joining strings:

• Characters are typically separated by whitespace characters such as blank, tab, and newline, though other characters may be used - these are called delimiters.
• To tokenize using .split just add a delimiter (', ').
• rsplit method performs the same task as split but processes the maximum number of splits from the end of the string to the beginning.
• Join concatenates the strings in its argument, which must be an iterable containing only string values.
• partition splits a string into a tuple of three strings based on the method's separator argument.
• To search for the separator from the end, use rpartition.
• Method splitlines returns a list of new strings representing the lines of text split at each newline character in the original string.

Characters and Character-Testing Methods:

• Characters are fundamental building blocks of programs.
• String method isdigit returns true if the string on which you call the method contains only the digit characters.
• Another list of is functions on page 298.

Raw Strings:

• raw strings preceded by character r treat a file path that uses \.

Introduction to Regular Expressions:

• A regular expression string describes a search pattern for matching characters in other strings.