Starting With Data
Last updated on 2024-02-13 | Edit this page
Overview
Questions
- How can I import data in Python?
- What is Pandas?
- Why should I use Pandas to work with data?
Objectives
- Navigate the workshop directory and download a dataset.
- Explain what a library is and what libraries are used for.
- Describe what the Python Data Analysis Library (Pandas) is.
- Load the Python Data Analysis Library (Pandas).
- Use
read_csvto read tabular data into Python. - Describe what a DataFrame is in Python.
- Access and summarize data stored in a DataFrame.
- Define indexing as it relates to data structures.
- Perform basic mathematical operations and summary statistics on data in a Pandas DataFrame.
- Create simple plots.
Working With Pandas DataFrames in Python
We can automate the processes listed above using Python. It is efficient to spend time building code to perform these tasks because once it is built, we can use our code over and over on different datasets that share a similar format. This makes our methods easily reproducible. We can also share our code with colleagues so they can replicate our analysis.
Starting in the same spot
To help the lesson run smoothly, let’s ensure everyone is in the same directory. This should help us avoid path and file name issues. At this time please navigate to the workshop directory. If you working in IPython Notebook be sure that you start your notebook in the workshop directory.
A quick aside that there are Python libraries like OS Library that can work with our directory structure, however, that is not our focus today.
Alex’s Processing
Alex is a researcher who is interested in Early English books. Alex knows the EEBO dataset and refers to it to find data such as titles, places, and authors. As well as searching for titles, they want to create some exploratory plots and intermediate datasets.
Alex can do some of this work using spreadsheet systems but this can be time consuming to do and revise. It can lead to mistakes that are hard to detect.
The next steps show how Python can be used to automate some of the processes.
As a result of creating Python scripts, the data can be re-run in the future.
Our Data
For this lesson, we will be using the EEBO catalogue data, a subset of the data from EEBO/TCP Early English Books Online/Text Creation Partnership
We will be using files from the data folder. This section will use
the eebo.csv file that can be found in your data
folder.
We are studying the authors and titles published marked up by the
Text Creation Partnership. The dataset is stored as a comma separated
(.csv) file, where each row holds information for a single
title, and the columns represent diferent aspects (variables) of each
entry:
| Column | Description |
|---|---|
| TCP | TCP identity |
| EEBO | EEBO identity |
| VID | VID identity |
| STC | STC identity |
| status | Whether the book is free or not |
| Author | Author(s) |
| Date | Date of publication |
| Title | The Book title |
| Terms | Terms associated with the text |
| Page Count | Number of pages in the text |
| Place | Location where the work was published |
If we open the eebo.csv data file using a text editor,
the first few rows of our first file look like this:
TCP,EEBO,VID,STC,Status,Author,Date,Title,Terms,Page Count,Place
A00002,99850634,15849,STC 1000.5; ESTC S115415,Free,"Aylett, Robert, 1583-1655?",1625,"The brides ornaments viz. fiue meditations, morall and diuine. 1. Knowledge, 2. zeale, 3. temperance, 4. bountie, 5. ioy.",,134,London
A00005,99842408,7058,STC 10000; ESTC S106695,Free,"Higden, Ranulf, d. 1364. Polycronicon. English. Selections.; Trevisa, John, d. 1402.",1515,Here begynneth a shorte and abreue table on the Cronycles ...; Saint Albans chronicle.,Great Britain -- History -- To 1485 -- Early works to 1800.; England -- Description and travel -- Early works to 1800.,302,London
A00007,99844302,9101,STC 10002; ESTC S108645,Free,"Higden, Ranulf, d. 1364. Polycronicon.",1528,"The Cronycles of Englonde with the dedes of popes and emperours, and also the descripcyon of Englonde; Saint Albans chronicle.",Great Britain -- History -- To 1485 -- Early works to 1800.; England -- Description and travel -- Early works to 1800.,386,London
A00008,99848896,14017,STC 10003; ESTC S113665,Free,"Wood, William, fl. 1623, attributed name.",1623,Considerations vpon the treaty of marriage between England and Spain,Great Britain -- Foreign relations -- Spain.,14,The Netherlands?
A00011,99837000,1304,STC 10008; ESTC S101178,Free,,1640,"Englands complaint to Iesus Christ, against the bishops canons of the late sinfull synod, a seditious conuenticle, a packe of hypocrites, a sworne confederacy, a traiterous conspiracy ... In this complaint are specified those impieties and insolencies, which are most notorious, scattered through the canons and constitutions of the said sinfull synod. And confuted by arguments annexed hereunto.",Church of England. -- Thirty-nine Articles -- Controversial literature.; Canon law -- Early works to 1800.,54,Amsterdam
A00012,99853871,19269,STC 1001; ESTC S118664,Free,"Aylett, Robert, 1583-1655?",1623,"Ioseph, or, Pharoah's fauourite; Joseph.",Joseph -- (Son of Jacob) -- Early works to 1800.,99,London
A00014,33143147,28259,STC 10011.6; ESTC S3200,Free,,1624,Greate Brittaines noble and worthy councell of warr,"England and Wales. -- Privy Council -- Portraits.; Great Britain -- History -- James I, 1603-1625.; Broadsides -- London (England) -- 17th century.",1,London
A00015,99837006,1310,STC 10011; ESTC S101184,Free,"Jones, William, of Usk.",1607,"Gods vvarning to his people of England By the great ouer-flowing of the vvaters or floudes lately hapned in South-wales and many other places. Wherein is described the great losses, and wonderfull damages, that hapned thereby: by the drowning of many townes and villages, to the vtter vndooing of many thousandes of people.",Floods -- Wales -- Early works to 1800.,16, London
A00018,99850740,15965,STC 10015; ESTC S115521,Free,,1558,The lame[n]tacion of England; Lamentacion of England.,"Great Britain -- History -- Mary I, 1553-1558 -- Early works to 1800.",26,Germany?About Libraries
A library in Python contains a set of tools (functions) that perform different actions on our data. Importing a library is like getting a set of particular tools out of a storage locker and setting them up on the bench for use in a project. Once a library is set up, its functions can be used or called to perform different tasks.
Pandas in Python
One of the best options for working with tabular data in Python is to use the Python Data Analysis Library (a.k.a. Pandas Library). The Pandas library provides structures to sort our data, can produce high quality plots in conjunction with other libraries such as matplotlib, and integrates nicely with libraries that use NumPy (which is another common Python library) arrays.
Python doesn’t load all of the libraries available to it by default.
We have to add an import statement to our code in order to
use library functions required for our project. To import a library, we
use the syntax import libraryName, where
libraryName represents the name of the specific library we
want to use. Note that the import command calls a library
that has been installed previously in our system. If we use the
import command to call for a library that does not exist in
our local system, the command will throw and error when executed. In
this case, you can use pip command in another termina
window to install the missing libraries. See
here for details on how to do this.
Moreover, if we want to give the library a nickname to shorten the
command, we can add as nickNameHere. An example of
importing the pandas library using the common nickname pd
is:
Each time we call a function that’s in a library, we use the syntax
LibraryName.FunctionName. Adding the library name with a
. before the function name tells Python where to find the
function. In the example above, we have imported Pandas as
pd. This means we don’t have to type out
pandas each time we call a Pandas function.
Reading CSV Data Using Pandas
We will begin by locating and reading our survey data which are in
CSV format. We can use Pandas’ read_csv function to pull
either a local (a file in our machine) or a remote (one that is
available for downloading from the web) file into a Pandas table or DataFrame.
In order to read data in, we need to know where the data is stored on your computer or its URL address if the file is available on the web. It is recommended to place the data files in the same directory as the Jupyter notebook if working with local files
PYTHON
# note that pd.read_csv is used because we imported pandas as pd
# note that this assumes that the data file is in the same location
# as the Jupyter notebook
pd.read_csv("eebo.csv")So What’s a DataFrame?
A DataFrame is a 2-dimensional data structure that can store data of
different types (including characters, integers, floating point values,
factors and more) in columns. It is similar to a spreadsheet or a
data.frame in R. A DataFrame always has an index (0-based).
An index refers to the position of an element in the data structure.
In a terminal window the above command yields the output below. However, the output may differ slightly if using Jupyter Notebooks:
TCP EEBO VID \
0 A00002 99850634.0 15849
1 A00005 99842408.0 7058
2 A00007 99844302.0 9101
3 A00008 99848896.0 14017
4 A00011 99837000.0 1304
...
146 A00525 99856552 ... 854 Prentyd London
147 A00527 99849909 ... 72 London
148 A00535 99849912 ... 106 Saint-Omer
[148 rows x 11 columns]We can see that there were 149 rows parsed. Each row has 11 columns.
The first column displayed is the index of the
DataFrame. The index is used to identify the position of the data, but
it is not an actual column of the DataFrame. It looks like the
read_csv function in Pandas read our file properly.
However, we haven’t saved any data to memory so we can not work with it
yet. We need to assign the DataFrame to a variable so we can call and
use the data. Remember that a variable is a name given to a value, such
as x, or data. We can create a new object with
a variable name by assigning a value to it using =.
Let’s call the imported survey data eebo_df:
Notice that when you assign the imported DataFrame to a variable,
Python does not produce any output on the screen. We can print the value
of the eebo_df object by typing its name into the Python
command prompt.
which prints contents like above
Manipulating Our Index Data
Now we can start manipulating our data. First, let’s check the data
type of the data stored in eebo_df using the
type method. The type method and
__class__ attribute tell us that
eebo_df is
<class 'pandas.core.frame.DataFrame'>.
We can also enter eebo_df.dtypes at our prompt to view
the data type for each column in our DataFrame. int64
represents numeric integer values - int64 cells can not
store decimals. object represents strings (letters and
numbers). float64 represents numbers with decimals.
eebo_df.dtypeswhich returns:
TCP object
EEBO int64
VID int64
STC object
Status object
Author object
Date int64
Title object
Terms object
Page Count int64
Place object
dtype: objectWe’ll talk a bit more about what the different formats mean in a different lesson.
Useful Ways to View DataFrame objects in Python
We can use attributes and methods provided by the DataFrame object to summarize and access the data stored in it.
To access an attribute, use the DataFrame object name followed by the
attribute name df_object.attribute. For example, we can
access the Index
object containing the column names of eebo_df by using
its columns attribute
As we will see later, we can use the contents of the Index object to extract (slice) specific records from our DataFrame based on their values.
Methods are called by using the syntax
df_object.method(). Note the inclusion of open brackets at
the end of the method. Python treats methods as
functions associated with a dataframe rather than just
a property of the object as with attributes. Similarly to functions,
methods can include optional parameters inside the brackets to change
their default behaviour.
As an example, eebo_df.head() gets the first few rows in
the DataFrame eebo_df using the head()
method. With a method, we can supply extra information within
the open brackets to control its behaviour.
Let’s look at the data using these.
Challenge - DataFrames
Using our DataFrame eebo_df, try out the attributes
& methods below to see what they return.
eebo_df.columnseebo_df.shapeTake note of the output ofshape- what format does it return the shape of the DataFrame in?
HINT: More on tuples, here.
eebo_df.head()Also, what doeseebo_df.head(15)do?eebo_df.tail()
Calculating Statistics From Data In A Pandas DataFrame
We’ve read our data into Python. Next, let’s perform some quick summary statistics to learn more about the data that we’re working with. We might want to know how many different authors are included in our dataset, or how many works were published in a given year. We can perform summary stats quickly using groups. But first we need to figure out what we want to group by.
Let’s begin by exploring our data:
which returns:
array(['TCP', 'EEBO', 'VID', 'STC', 'Status', 'Author', 'Date', 'Title',
'Terms', 'Page Count', 'Place'], dtype=object)Let’s get a list of all the page counts. The pd.unique
function tells us all of the unique values in the Pages
column.
which returns:
PYTHON
array([134, 302, 386, 14, 54, 99, 1, 16, 26, 62, 50, 66, 30,
6, 36, 8, 12, 24, 22, 7, 20, 40, 38, 13, 28, 10,
23, 2, 112, 18, 4, 27, 42, 17, 46, 58, 200, 158, 65,
96, 178, 52, 774, 81, 392, 74, 162, 56, 100, 172, 94, 79,
107, 48, 102, 343, 136, 70, 156, 133, 228, 357, 110, 72, 44,
43, 37, 98, 566, 500, 746, 884, 254, 618, 274, 188, 374, 47,
34, 177, 82, 78, 64, 124, 80, 108, 182, 120, 68, 854, 106])Groups in Pandas
We often want to calculate summary statistics grouped by subsets or attributes within fields of our data. For example, we might want to calculate the average number of pages of the works in our DataFrame.
We can calculate basic statistics for all records in a single column by using the syntax below:
gives output
PYTHON
count 149.000000
mean 104.382550
std 160.125398
min 1.000000
25% 16.000000
50% 52.000000
75% 108.000000
max 884.000000
Name: Page Count, dtype: float64We can also extract specific metrics for one or various columns if we wish:
PYTHON
eebo_df['Page Count'].min()
eebo_df['Page Count'].max()
eebo_df['Page Count'].mean()
eebo_df['Page Count'].std()
eebo_df['Page Count'].count()But if we want to summarize by one or more variables, for example
author or publication date, we can use the .groupby
method. When executed, this method creates a DataFrameGroupBy
object containing a subset of the original DataFrame. Once we’ve created
it, we can quickly calculate summary statistics by a group of our
choice. For example the following code will group our data by place of
publication.
If we execute the pandas function
describe on this new object we will obtain
descriptive stats for all the numerical columns in eebo_df
grouped by the different cities available in the Place
column of the DataFrame.
PYTHON
# summary statistics for all numeric columns by place
grouped_data.describe()
# provide the mean for each numeric column by place
grouped_data.mean()grouped_data.mean() OUTPUT:
PYTHON
EEBO ... Page Count
Place ...
Amsterdam 9.983700e+07 ... 54.000000
Antverpi 9.983759e+07 ... 34.000000
Antwerp 9.985185e+07 ... 40.000000
Cambridge 2.445926e+07 ... 1.000000
Emden 9.984713e+07 ... 58.000000The groupby command is powerful in that it allows us to
quickly generate summary stats.
Challenge - Summary Data
- What is the mean page length for books published in
Amsterdamand how many forLondon - What happens when you group by two columns using the following syntax and then grab mean values:
grouped_data2 = eebo_df.groupby(['EEBO','Page Count'])grouped_data2.mean()
- Summarize the Date values in your data. HINT: you can use the
following syntax to only create summary statistics for one column in
your data
eebo_df['Page Count'].describe()
A Snippet of the Output from challenge 3 looks like:
count 149.000000
mean 1584.288591
std 36.158864
min 1515.000000
25% 1552.000000
50% 1583.000000
75% 1618.000000
max 1640.000000
...Quickly Creating Summary Counts in Pandas
Let’s next count the number of samples for each author. We can do
this in a few ways, but we’ll use groupby combined with
a count() method.
PYTHON
# count the number of texts by authors
author_counts = eebo_df.groupby('Author')['EEBO'].count()
print(author_counts)Or, we can also count just the rows that have the author “A. B.”:
Quick & Easy Plotting Data Using Pandas
We can plot our summary stats using Pandas, too.
PYTHON
# when using a Jupyter notebook, force graphs to appear in line
%matplotlib inline
# Collect data together
date_count = eebo_df.groupby("Date")["Status"].count()
date_count.plot(kind="bar")
What does this graph show? Let’s step through
-
eebo_df.groupby("Date"): This groups the texts by the date in which they were published -
eebo_df.groupby("Date")["Status"]: This chooses a single column to count, rather than counting all columns -
eebo_df.groupby("Date")["Status"].count(): this counts the instances, i.e. how many texts in a given year have a status? -
date_count.plot(kind="bar"): this plots that data as a bar chart
> ## Challenge - Plots
>
> 1. Create a plot of Authors across all Places per plot. Does it look how you
expect it to look?
{: .challenge}
> ## Summary Plotting Challenge
>
> Create a stacked bar plot, showing the total pages published, per year, with
> the different publishing locations stacked on top of each other. The Date
> should go on the X axis, and the Page Count on the Y axis. Some tips are below
> to help you solve this challenge:
>
> * [For more on Pandas plots, visit this link.](http://pandas.pydata.org/pandas-docs/stable/visualization.html#basic-plotting-plot)
> * You can use the code that follows to create a stacked bar plot but the data to stack
> need to be in individual columns. Here's a simple example with some data where
> 'a', 'b', and 'c' are the groups, and 'one' and 'two' are the subgroups.
>
> ```
> d = {'one' : pd.Series([1., 2., 3.], index=['a', 'b', 'c']),'two' : pd.Series([1., 2., 3., 4.], index=['a', 'b', 'c', 'd'])}
> pd.DataFrame(d)
> ```
>
> shows the following data
>
> ```
> one two
> a 1 1
> b 2 2
> c 3 3
> d NaN 4
> ```
>
> We can plot the above with
>
> ```
> # plot stacked data so columns 'one' and 'two' are stacked
> my_df = pd.DataFrame(d)
> my_df.plot(kind='bar', stacked=True, title="The title of my graph")
> ```
>
> 
>
> * You can use the `.unstack()` method to transform grouped data into columns
> for each plotting. Try running `.unstack()` on some DataFrames above and see
> what it yields.
>
> Start by transforming the grouped data into an unstacked layout, then create
> a stacked plot.
>
>
>> ## Solution to Summary Challenge
>>
>> First we group data by date and then by place.
>>
>> ```python
>> date_place = eebo_df.groupby(['Date','Place'])
>> page_sum = date_place['Page Count'].sum()
>> ```
>>
>> This calculates the sums for each place, for each date, as a table
>>
>> ```
>> page_sum
>> Date Place
>> 1515 London 302
>> 1519 Londini 74
>> 1526 London 150
>> 1528 London 386
>> <other plots removed for brevity>
>> ```
>>
>> After that, we use the `.unstack()` function on our grouped data to figure
>> out the total contribution of each place, to each year, and then plot the
>> data
>> ```python
>> table = page_sum.unstack()
>> plot = table.plot(kind="bar", stacked=True, title="Pages published per year", figsize=(10,5))
>> plot.set_ylabel("Pages")
>> ```
> {: .solution}
{: .challenge}