# 4.1 Week 4 glossary
Here is an alphabetical list of the terms introduced this week, for quick look-up.
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### Programming and data analysis concepts
The __bitwise operators__``&`` (and) and ``|`` (or) are used in pandas to build more complicated expressions from two comparison expressions (typically involving column comparisons).
A __Boolean__ has one of two possible values: ``True`` or ``False``.
A __Comma Separated Values (CSV)__ file is a plain text file that is used to hold tabular data.
A __list__ is a sequence of values, separated by commas, and written within square brackets.
There are six __comparison operators__ that can be used to compare number, string and date values. Expressions composed of these operators evaluate to ``True`` or ``False``. These operators can also be used to compare every value in a column, row by row, against some number, string or date value. When used in this manner the operators return a series of Boolean values.
The __‘dot’ notation__ is used to access a dataframe’s methods and attributes.
The ``Series`` data type is a collection of values with an integer index that starts from zero. Each column in a dataframe is an example of the ``Series`` data type. The ``Series`` data type has many of the same methods as the ``DataFrame`` data type.
The ``object`` data type is how pandas represents strings.
The ``datetime64`` data type is how pandas represents dates.
The ``int64`` data type is how pandas represents integers (whole numbers).
The ``float64`` data type is how pandas represents floating point numbers (decimals).
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### Functions and methods
``asType(aType)`` when applied to a dataframe column, the method changes the data type of each value in that column to the type given by the string ``aType``.
``datetime(yyyy, mm, dd)`` the function takes three arguments, ``yyyy`` a four digit integer representing a year, ``mm`` a two digit integer representing a month and ``dd`` a two digit integer representing a day. From these arguments the function creates and returns a value of ``datetime64``.
``dropna()`` when applied to a dataframe returns a new dataframe without the rows that have at least one missing value.
``head()`` gets and displays the first five rows of a dataframe. Optionally the method can take an integer argument to specify how many rows (from and including row 0) to get and display.
``iloc[index]`` gets and displays the row in the dataframe indicated by the integer argument ``index``.
``isnull()`` is a series method that checks which rows in that series have a missing value.
``fillna(value)`` is a series method that returns a new series in which all missing values have been filled with the given value.
``plot()`` when applied to a dataframe column of numeric values, the method displays a graph of those values. The x-axis shows the dataframe’s index and the y-axis the range of the column’s values. Before the method is called you first need to execute ``%matplotlib inline``.
``read_csv(csvFile)`` creates a dataframe from the dataset in the CSV file.
``rename(columns={oldName : newName})`` renames the column ``oldName`` to ``newName``.
``str.rstrip(suffix)`` when applied to a dataframe column of string values, the method removes the argument ``suffix`` from the end of each string value in the column.
``tail()`` gets and displays the last five rows of a dataframe. Optionally the method can take an integer argument to specify how many rows (until and including the last row) to get and display.
``to_datetime(aSeries)`` when applied to a series, typically a column from a dataframe, this function returns a new series in which each value in ``aSeries`` has been changed to type ``datetime64``.
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