4.1 Week 4 glossary

Here is an alphabetical list of the terms introduced this week, for quick look-up.

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).

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__

.