Pandas

Question 1

pandas

Answer 1

The most popular library for data analysis in python

Question 2

What are the two core objects in pandas?

Answer 2

DataFrame

Series

Question 3

Line of code to import pandas

Answer 3

import pandas as pd

Question 4

DataFrame

Answer 4

A DataFrame is a table, it contains an array of individual entries each of which has a certain value and corresponds to a row and a column

It can be thought of as a bunch of Series joined together

pd.DataFrame({‘Yes’: [50, 21], ‘No’: [131, 2]})

    Yes	No 0	50	131 1	21	2

Question 5

pd.DataFrame()

Answer 5

This is a standard method of producing dataframes

Within the brackets you place a dictionary in which the column names are the keys and whose values are the entries

The list of row labels is known as an index, and it can be used in the dictionary to outline the names of the rows

pd.DataFrame({‘Bob’: [‘I liked it.’, ‘It was awful.’],
‘Sue’: [‘Pretty good.’, ‘Bland.’]},
index=[‘Product A’, ‘Product B’])
Bob Sue
Product A I liked it. Pretty good.
Product B It was awful. Bland.

Question 6

Series

Answer 6

A sequence of data values

If a DataFrame is a table, a Series is a list. It is in essence a single column of a DataFrame

You can assign row names using the same method as with a DataFrame and the single column name can be assigned using name

e.g. pd.Series([1, 2, 3], index = [‘Mon’, ‘Tue’, ‘Wed’], name = Date)

Question 7

Reading a dataset into a DataFrame and checking the shape of the data

Answer 7

we use pd.read_csv(file_name)

we then use the .shape attribute to show the shape of the data dataframename.shape

This will return the tuple in the form (number of rows, number of columns)

Question 8

DataFrameName.head()

Answer 8

Shows the first 5 rows of the DataFrame

Question 9

Saving a DataFrame as a csv

Answer 9

DataFrameName.to_csv(‘file_name.csv’)

Question 10

How to access the data held in a column of a dataframe? / How to access a series of a dataframe?

Answer 10

We can access it the same way way we would for values in dictionary e.g. DataFrameName[‘column_name’]

Or we can use dot notation, which is like accessing an attribute/property of a class

DataFrameName.column_name

Question 11

Accessing an individual data point from a DataFrame?

Answer 11

We use chaining of indices

e.g. DataFrameName[‘column_name’][index number]

We are following the order column first, row second

Question 12

Using iloc

Answer 12

This is one of pandas methods of retrieving data or columns from a dataframe, they work in the opposite way - row first, column second

It uses indices the same way python normally uses indices

e.g. DataFrame.iloc[row index, column index]

we can still use index slicing etc and we can also pass a list of indices

Question 13

Using loc

Answer 13

Similar to iloc and again is a way of accessing data but it is a bit simpler

DataFrameName.loc[row number, column_name]

loc uses indexes differently, it uses them inclusively e.g. [0:10] would return all the rows from 0 to 10 including 0 and 10

Question 14

Manipulating the index by setting the rows of the first column the index

Answer 14

We can use the set_title() method

e.g. DataFrameName.set_title(‘column_name’, inplace = True)

making inplace = True ensure this method makes changes to the dataset

this method will make the rows of the column selected, the new indices

Question 15

Using pipe | and ampersand for conditional selection

Answer 15

& is used when we are trying to select with this property AND that property

is used when we are saying we want to select data that has this property OR that property

Question 16

isin() method

Answer 16

This lets you select data whose value is in a list of values

We could combine this with loc as follows

DataFrameName.loc[DataFrameName.column_name.isin([list if values we want that are in the column]]

Question 17

isnull() method and notnull() method

Answer 17

These two methods help identify all the rows that have no data in a specific column or those that do have data

e.g. DataFrameName.loc[DataFrameName.column_name.isnull()]

Question 18

describe() method

Answer 18

Provides a high level summary of a specific column of data including the mean and quartiles etc

e.g. DataFrameName.column_name.describe()

Question 19

unique() method

Answer 19

Provides a list of all the unique values in a column

e.g. DataFrameName.column_name.unique()

Question 20

value_counts() method

Answer 20

Returns a list of the unique values and how often they occur for a particular column

e.g. df_name.column_name.value_counts()

Question 21

map() function

Answer 21

Used to substitute the values of a series in a data table in accordance to the other input which could be a function

The function you pass to map() should expect a single value from the Series, and return a transformed version of that value. map() returns a new Series where all the values have been transformed by your function.

e.g. df_name.column_name.map(function)

Question 22

apply()

Answer 22

Similar to map() except it is used to transform the whole DataFrame using a custom function

It takes the custom function as one of its arguments and axis = 0 or 1 or ‘columns’

When axis = 0 it applies the function to each column, when its 1 it applies the function to each row

When axis = ‘columns’ it applies the function to each row and when axis = ‘index’ it applies the function to each column

e.g. dataframe.apply(customfunction, axis)

or can be used for individual column dataframe.column,apply(function)

Question 23

idxmax() method

Answer 23

Used to get the row label/the index of the maximum value in a series

Series.idxmax(self, axis, skipna)

axis is only used if we are applying idxmax to the whole dataframe

if skipna = True, then the function will not include NA values

Question 24

groupby() method

Answer 24

Used to split the data into groups based on the criteria entered within the brackets such as column names etc

Sometimes produces a multi index

Question 25

agg() method

Answer 25

Lets you run multiple functions on your dataset at once

Question 26

reset_index() method

Answer 26

Used to convert a multi index to a regular index

Question 27

sort_values() method

Answer 27

Used to sort a series into an order based on the values it contains, by default this is in ascending order

dataframe.sort_values(by = ‘len’, ascending = True)
We use by to determine how we are sorting the values and if ascending is set to false it will be in descending order

Question 28

sort_index() method

Answer 28

Used to sort a series into order based on index of rows

e.g. dataframe.sort_index(axis = ‘column name’ or index, ascending = true or false)