Pandas Basics — Part 2#
Note: You can explore the associated workbook for this chapter in the cloud.
In this lesson, we’re going to introduce some more fundamentals of Pandas, a powerful Python library for working with tabular data like CSV files.
We will review skills learned from the last lesson and introduce how to:
Broadly examine data
Work with missing data
Rename, drop, and add new columns
Perform mathematical calculations
Aggregate subsets of data
Make a simple time series
Dataset#
The Trans-Atlantic Slave Trade Database#
[D]isplaying data alone could not and did not offer the atonement descendants of slaves sought or capture the inhumanity of this archive’s formation.
-Jessica Marie Johnson, “Markup Bodies”
The dataset that we’re going to be working with in this lesson is taken from The Trans-Atlantic Slave Trade Database, part of the Slave Voyages project. The larger database includes information about 35,000 slave-trading voyages from 1514-1866. The dataset we’re working with here was filtered to include the 20,000 voyages that landed in the Americas. The data was filtered to also include the percentage of enslaved men, women, and children on the voyages.
We’re working with this data for a number of reasons. The Slave Voyages project is a major data-driven contribution to the history of slavery and to the field of the digital humanities. Before the Trans-Atlantic Slave Trade Database, as DH scholar Jessica Johnson writes, “historians assumed enslaved women and children played a negligible role in the slave trade.” But evidence from the Trans-Atlantic Slave Trade Database suggested otherwise. “The existence of the Trans-Atlantic Slave Trade Database immediately reshaped debates about numbers of women and children exported from the continent,” Johnson says, “influencing work on women in the slave trade on the African coast, slavery in African societies, and women in the slave trade to the Americas.”
Though the Trans-Atlantic Slave Trade Database helped shed new light on the roles of enslaved women and children, Johnson makes clear that it was not computation or data alone that shed this light:
[D]isplaying data alone could not and did not offer the atonement descendants of slaves sought or capture the inhumanity of this archive’s formation. Culling the lives of women and children from the data set required approaching the data with intention. It required a methodology attuned to black life and to dismantling the methods used to create the manifests in the first place, then designing and launching an interface responsive to the desire of descendants of slaves for reparation and redress.
In this spirit, we want to think about how responsible data analysis requires more than just data and technical tools like Pandas. It requires approaching data with intention and developing methodologies geared toward justice. This is especially necessary when dealing with data that records and perpetrates violence like the Trans-Atlantic Slave Trade Database.
Import Pandas
To use the Pandas library, we first need to import it.
import pandas as pd
The above import statement not only imports the Pandas library but also gives it an alias or nickname — pd. This alias will save us from having to type out the entire words pandas each time we need to use it. Many Python libraries have commonly used aliases like pd.
Set Display Settings
By default, Pandas will display 60 rows and 20 columns. I often change Pandas’ default display settings to show more rows or columns.
pd.options.display.max_rows = 100
Read in CSV File
Tip
If you use the help() function, you can see the documentation for almost any bit of code. If we run it on pd.read_csv(), we can see all the possible parameters that can be used with pd.read_csv().
help(pd.read_csv)
To read in a CSV file, we will use the function pd.read_csv() and insert the name of our desired file path.
slave_voyages_df = pd.read_csv('../data/Trans-Atlantic-Slave-Trade_Americas.csv', delimiter=",", encoding='utf-8')
This creates a Pandas DataFrame object — often abbreviated as df, e.g., slave_voyages_df. A DataFrame looks and acts a lot like a spreadsheet. But it has special powers and functions that we will discuss in the next few lessons.
When reading in the CSV file, we also specified the encoding and delimiter. The delimiter specifies the character that separates or “delimits” the columns in our dataset. For CSV files, the delimiter will most often be a comma. (CSV is short for Comma Separated Values.) Sometimes, however, the delimiter of a CSV file might be a tab (\t) or, more rarely, another character.
Display Data
We can display a DataFrame in a Jupyter notebook simply by running a cell with the variable name of the DataFrame.
Pandas Review
NaN is the Pandas value for any missing data. See “Working with missing data” for more information.
slave_voyages_df
| year_of_arrival | flag | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | sources | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1520 | NaN | Portuguese Guinea | San Juan | NaN | NaN | NaN | 324.0 | 259.0 | NaN | NaN | 42987 | [u'AGI,Patronato 175, r.9<><p><em>AG!</em> (Se... | |
| 1 | 1525 | Portugal / Brazil | Sao Tome | Hispaniola, unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | S Maria de Bogoña | Monteiro, Pero | 46473 | [u'ANTT,CC,Parte II, maco 131, doc 54<><i>Inst... |
| 2 | 1526 | Spain / Uruguay | Cape Verde Islands | Cuba, port unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | Carega, Esteban (?) | 11297 | [u'Pike,60-1,172<>Pike, Ruth, <i>Enterprise</i... | |
| 3 | 1526 | Spain / Uruguay | Cape Verde Islands | Cuba, port unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | Carega, Esteban (?) | 11298 | [u'Pike,60-1,172<>Pike, Ruth, <i>Enterprise</i... | |
| 4 | 1526 | NaN | Cape Verde Islands | Caribbean (colony unspecified) | NaN | NaN | NaN | 359.0 | 287.0 | NaN | S Anton | Leon, Juan de | 42631 | [u'Chaunus, 3: 162-63<><p>Chaunus, <em>xxxxxx<... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20736 | 1864 | Spain / Uruguay | Africa., port unspecified | Cuba, port unspecified | NaN | NaN | NaN | 488.0 | 465.0 | NaN | Polaca | NaN | 46554 | [u'AHNM, Ultramar, Leg. 3551, 6<><i>Archivo Hi... |
| 20737 | 1865 | Spain / Uruguay | Africa., port unspecified | Isla de Pinas | NaN | NaN | NaN | 152.0 | 145.0 | Slave insurrection | Gato | NaN | 4394 | [u'IUP,ST,50/B/137<>Great Britain, <i>Irish Un... |
| 20738 | 1865 | NaN | Africa., port unspecified | Mariel | NaN | NaN | NaN | 780.0 | 650.0 | NaN | NaN | 4395 | [u'IUP,ST,50/B/144<>Great Britain, <i>Irish Un... | |
| 20739 | 1865 | NaN | Congo River | Cuba, port unspecified | NaN | NaN | NaN | 1265.0 | 1004.0 | NaN | Cicerón | Mesquita | 5052 | [u'IUP,ST,50/A/23-4<>Great Britain, <i>Irish U... |
| 20740 | 1866 | NaN | Africa., port unspecified | Cuba, port unspecified | NaN | NaN | NaN | 851.0 | 700.0 | NaN | NaN | 4998 | [u'IUP,ST,50/B/220<>Great Britain, <i>Irish Un... |
20741 rows × 14 columns
There are a few important things to note about the DataFrame displayed here:
Index
The bolded ascending numbers in the very left-hand column of the DataFrame is called the Pandas Index. You can select rows based on the Index.
By default, the Index is a sequence of numbers starting with zero. However, you can change the Index to something else, such as one of the columns in your dataset.
Truncation
The DataFrame is truncated, signaled by the ellipses in the middle
...of every column.The DataFrame is truncated because we set our default display settings to 100 rows. Anything more than 100 rows will be truncated. To display all the rows, we would need to alter Pandas’ default display settings yet again.
Rows x Columns
Pandas reports how many rows and columns are in this dataset at the bottom of the output (20,741 x 14 columns).
Display First n Rows
To look at the first n rows in a DataFrame, we can use a method called .head().
slave_voyages_df.head(10)
| year_of_arrival | flag | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | sources | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1520 | NaN | Portuguese Guinea | San Juan | NaN | NaN | NaN | 324.0 | 259.0 | NaN | NaN | 42987 | [u'AGI,Patronato 175, r.9<><p><em>AG!</em> (Se... | |
| 1 | 1525 | Portugal / Brazil | Sao Tome | Hispaniola, unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | S Maria de Bogoña | Monteiro, Pero | 46473 | [u'ANTT,CC,Parte II, maco 131, doc 54<><i>Inst... |
| 2 | 1526 | Spain / Uruguay | Cape Verde Islands | Cuba, port unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | Carega, Esteban (?) | 11297 | [u'Pike,60-1,172<>Pike, Ruth, <i>Enterprise</i... | |
| 3 | 1526 | Spain / Uruguay | Cape Verde Islands | Cuba, port unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | Carega, Esteban (?) | 11298 | [u'Pike,60-1,172<>Pike, Ruth, <i>Enterprise</i... | |
| 4 | 1526 | NaN | Cape Verde Islands | Caribbean (colony unspecified) | NaN | NaN | NaN | 359.0 | 287.0 | NaN | S Anton | Leon, Juan de | 42631 | [u'Chaunus, 3: 162-63<><p>Chaunus, <em>xxxxxx<... |
| 5 | 1526 | NaN | Cape Verde Islands | San Domingo (a) Santo Domingo | NaN | NaN | NaN | 359.0 | 287.0 | NaN | Santa Maria de Guadalupe | Pabon, Francisco | 42679 | [u'Chaunus, 3: 162-63<><p>Chaunus, <em>xxxxxx<... |
| 6 | 1526 | Portugal / Brazil | Sao Tome | Spanish Caribbean, unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | NaN | 46474 | [u'ANTT,CC,Parte II, maco 131, doc 54<><i>Inst... | |
| 7 | 1527 | Spain / Uruguay | Cape Verde Islands | Puerto Rico, port unspecified | NaN | NaN | NaN | 325.0 | 260.0 | NaN | Concepción | Díaz, Alonso | 99027 | [u'SuedBadillo,57,75,76<><p>SuedBadillo, <em>x... |
| 8 | 1532 | Portugal / Brazil | Sao Tome | Spanish Caribbean, unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | S Antônio | Afonso, Martim | 11293 | [u'Ryder,66<>Ryder, A. F. C., <i>Benin</i><i> ... |
| 9 | 1532 | NaN | Cape Verde Islands | San Juan | NaN | NaN | NaN | 25.0 | 20.0 | NaN | de Illanes, Manuel | 28994 | [u'Tanodi, 321-22<>Tanodi, Aurelio, <i>Documen... |
Examine Data#
Shape#
To explicitly check for how many rows vs columns make up a dataset, we can use the .shape method.
slave_voyages_df.shape
(20741, 14)
There are 20,741 rows and 14 columns.
Data Types#
Just like Python has different data types, Pandas has different data types, too. These data types are automatically assigned to columns when we read in a CSV file. We can check these Pandas data types with the .dtypes method.
Pandas Data Type |
Explanation |
|---|---|
|
string |
|
float |
|
integer |
|
date time |
slave_voyages_df.dtypes
year_of_arrival int64
flag object
place_of_purchase object
place_of_landing object
percent_women float64
percent_children float64
percent_men float64
total_embarked float64
total_disembarked float64
resistance_label object
vessel_name object
captain's_name object
voyage_id int64
sources object
dtype: object
It’s important to always check the data types in your DataFrame. For example, sometimes numeric values will accidentally be interpreted as a string object. To perform calculations on this data, you would need to first convert that column from a string to an integer.
Columns#
We can also check the column names of the DataFrame with .columns
slave_voyages_df.columns
Index(['year_of_arrival', 'flag', 'place_of_purchase', 'place_of_landing',
'percent_women', 'percent_children', 'percent_men', 'total_embarked',
'total_disembarked', 'resistance_label', 'vessel_name',
'captain's_name', 'voyage_id', 'sources'],
dtype='object')
Summary Statistics#
slave_voyages_df.describe(include='all')
| year_of_arrival | flag | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | sources | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| count | 20741.000000 | 19583 | 20663 | 20741 | 2894.000000 | 2927.000000 | 2894.000000 | 20722.000000 | 20719.000000 | 372 | 20741 | 19396 | 20741.000000 | 20741 |
| unique | NaN | 8 | 156 | 187 | NaN | NaN | NaN | NaN | NaN | 6 | 5849 | 12233 | NaN | 13754 |
| top | NaN | Great Britain | Africa., port unspecified | Barbados, port unspecified | NaN | NaN | NaN | NaN | NaN | Slave insurrection | Smith, John | NaN | [u'mettas,I<>Mettas, Jean, <i>R\xe9pertoire d... | |
| freq | NaN | 10536 | 5999 | 2038 | NaN | NaN | NaN | NaN | NaN | 330 | 712 | 36 | NaN | 1134 |
| mean | 1752.014850 | NaN | NaN | NaN | 0.274198 | 0.231582 | 0.496648 | 295.050381 | 251.573966 | NaN | NaN | NaN | 42783.741671 | NaN |
| std | 59.702189 | NaN | NaN | NaN | 0.116513 | 0.149508 | 0.140324 | 147.997690 | 128.050439 | NaN | NaN | NaN | 32401.785320 | NaN |
| min | 1520.000000 | NaN | NaN | NaN | 0.000000 | 0.000000 | 0.000000 | 1.000000 | 1.000000 | NaN | NaN | NaN | 112.000000 | NaN |
| 25% | 1724.000000 | NaN | NaN | NaN | 0.195265 | 0.115380 | 0.407460 | 194.000000 | 163.000000 | NaN | NaN | NaN | 17862.000000 | NaN |
| 50% | 1765.000000 | NaN | NaN | NaN | 0.264110 | 0.215100 | 0.497890 | 282.000000 | 241.000000 | NaN | NaN | NaN | 31916.000000 | NaN |
| 75% | 1792.000000 | NaN | NaN | NaN | 0.346150 | 0.321900 | 0.586765 | 368.000000 | 313.000000 | NaN | NaN | NaN | 78283.000000 | NaN |
| max | 1866.000000 | NaN | NaN | NaN | 1.000000 | 1.000000 | 1.000000 | 2024.000000 | 1700.000000 | NaN | NaN | NaN | 900206.000000 | NaN |
Missing Data#
The conceit of the archive is that it is the repository of answers, of knowable conclusions, of the data needed to explain or understand the past.
The reality, however, is that the archive is the troubled genesis of our always-failed effort to unravel the effects of the past on the present; rather than verifiable truths, the archive — and its silences — house the very questions that unsettle us.
—Jennifer Morgan, “Accounting for ‘The Most Excruciating Torment’”
Responsible data analysis requires understanding missing data. The Trans-Atlantic Slave Trade Database, as historian Jennifer Morgan writes, contains innumerable “silences” and “gaps.” These silences include the thoughts, feelings, and experiences of the enslaved African people on board the voyages — silences that cannot be found in the database itself.
There are other kinds of silences and gaps that can be detected in the database itself, however. For example, while some of the voyages in the the Trans-Atlantic Slave Trade Database recorded information about how many enslaved women and children were aboard, most did not. Yet focusing on the data that is there and analyzing trends in the missing data can help shed light on the history of gender and enslavement. The fact that most ship captains did not record gender information, Morgan argues, helps tell us about their “priorities”: “[W]e can assume that had it been financially significant to have more men than women that data would have been more scrupulously recorded.”
.isna() / .notna()#
Pandas has special ways of dealing with missing data. As you may have already noticed, blank rows in a CSV file show up as NaN in a Pandas DataFrame.
To filter and count the number of missing/not missing values in a dataset, we can use the special .isna() and .notna() methods on a DataFrame or Series object.
slave_voyages_df['percent_women'].notna()
0 False
1 False
2 False
3 False
4 False
...
20736 False
20737 False
20738 False
20739 False
20740 False
Name: percent_women, Length: 20741, dtype: bool
The .isna() and .notna() methods return True/False pairs for each row, which we can use to filter the DataFrame for any rows that have information in a given column. For example, we can filter the DataFrame for only rows that have information about the percentage of enslaved women aboard the voyage.
slave_voyages_df[slave_voyages_df['percent_women'].notna()]
| year_of_arrival | flag | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | sources | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 938 | 1613 | Portugal / Brazil | Luanda | Santo Tomas | 0.30556 | 0.20588 | 0.69444 | 362.0 | 290.0 | NaN | NS de Nazareth | Gómez, Juan | 47352 | [u'AGI-Esc 38B, pieza 2, folios 427r-427v<><p>... |
| 1044 | 1619 | Portugal / Brazil | Luanda | Veracruz | 0.21127 | 0.21596 | 0.57277 | 349.0 | 279.0 | NaN | S Antônio | Acosta, Jacome de | 29248 | [u'Vila Vilar,Cuadro3<><p>Vila Vilar, Enriquet... |
| 1115 | 1620 | Portugal / Brazil | Luanda | Buenos Aires | 0.13043 | 0.29193 | 0.57764 | 381.0 | 304.0 | NaN | NS de Consolación | Acosta, Gonçalo | 29561 | [u'AGI, Indiferente General, 2795<><p><em>AG!<... |
| 1117 | 1620 | NaN | Luanda | Cumana | 0.29570 | 0.33571 | 0.70430 | 421.0 | 337.0 | NaN | NS de Rocha | Sosa, Nicolás de<br/> Estéves, Domingo<br/> Ro... | 29941 | [u'AGI, Contratacion, 2881<><p><em>AG!</em> (S... |
| 1334 | 1628 | Portugal / Brazil | West Central Africa and St. Helena, port unspe... | Spanish Circum-Caribbean,unspecified | 0.16908 | 0.58454 | 0.24638 | 303.0 | 242.0 | NaN | S Pedro | Silva, Jacinto da | 29568 | [u'AGI, Indiferente General, 2796<><p><em>AG!<... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20295 | 1841 | Portugal / Brazil | Rio Pongo | Cuba, port unspecified | 0.20548 | 0.21233 | 0.58219 | 324.0 | 292.0 | NaN | Segunda Rosália | Peirano, Francisco | 2078 | [u'PP,1845,XLIX:593-633<>Great Britain, <i>Par... |
| 20321 | 1841 | Spain / Uruguay | Africa., port unspecified | Bahamas, port unspecified | 0.15758 | 0.31548 | 0.53939 | 215.0 | 193.0 | NaN | Trovadore | Velasea, de Bonita | 5503 | [u'Dalleo,24<>Dalleo, Peter D.,"Africans in th... |
| 20429 | 1850 | NaN | Benguela | British Caribbean, colony unspecified | 0.00000 | 1.00000 | 0.00000 | 94.0 | 74.0 | NaN | Amélia | Oliveira, José | 4674 | [u'IUP,ST,38/A/208<>Great Britain, <i>Irish Un... |
| 20498 | 1854 | U.S.A. | Whydah | Bahia Honda | 0.45455 | 0.08333 | 0.54545 | 600.0 | 584.0 | NaN | Grey Eagle | Darnaud | 4190 | [u'FO84/965,Crawford,55.02.07,enc<><p><em>BNA<... |
| 20555 | 1857 | NaN | Cabinda | Kingston | 0.08943 | 0.00000 | 0.91057 | 500.0 | 362.0 | NaN | Zeldina | NaN | 4229 | [u'IUP,ST,44/A/44,161<>Great Britain, <i>Irish... |
2894 rows × 14 columns
The data is now filtered to only include the 2,894 rows with information about how many women were aboard the voyage.
To explicitly count the number of blank rows, we can use the .value_counts() method.
slave_voyages_df['percent_women'].isna().value_counts()
True 17847
False 2894
Name: percent_women, dtype: int64
There are 17,874 that do not contain information about the number of enslaved women on the voyage (isna = True) and 2,894 rows that do contain this information (isna = False).
To quickly transform these numbers into percentages, we can set the normalize= parameter to True.
slave_voyages_df['percent_women'].isna().value_counts(normalize=True)
True 0.86047
False 0.13953
Name: percent_women, dtype: float64
About 14% of rows in this dataset have information about the number of enslaved women on the voyage while 86% do not.
.count()#
Because the .count() method always excludes NaN values, we can also count the number of values in each column and divide by the total number of rows in each column (len()) to find the percentage of not blank data in every column.
slave_voyages_df.count() / len(slave_voyages_df)
year_of_arrival 1.000000
flag 0.944169
place_of_purchase 0.996239
place_of_landing 1.000000
percent_women 0.139530
percent_children 0.141121
percent_men 0.139530
total_embarked 0.999084
total_disembarked 0.998939
resistance_label 0.017935
vessel_name 1.000000
captain's_name 0.935153
voyage_id 1.000000
sources 1.000000
dtype: float64
For example, 100% of the rows in the columns “year_of_arrival” contain information, while 2% of the rows in the column “resistance_label” contain information. The “resistance_label” indicates whether there is a record of the enslaved Africans aboard the voyage staging some form of resistance.
.fillna()#
If we wanted, we could fill the NaN values in the DataFrame with a different value by using the .fillna() method.
slave_voyages_df['percent_women'].fillna('no gender information recorded')
0 no gender information recorded
1 no gender information recorded
2 no gender information recorded
3 no gender information recorded
4 no gender information recorded
...
20736 no gender information recorded
20737 no gender information recorded
20738 no gender information recorded
20739 no gender information recorded
20740 no gender information recorded
Name: percent_women, Length: 20741, dtype: object
Rename Columns#
We can rename columns with the .rename() method and the columns= parameter. For example, we can rename the “flag” column “national_affiliation.”
slave_voyages_df.rename(columns={'flag': 'national_affiliation'})
| year_of_arrival | national_affiliation | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | sources | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1520 | NaN | Portuguese Guinea | San Juan | NaN | NaN | NaN | 324.0 | 259.0 | NaN | NaN | 42987 | [u'AGI,Patronato 175, r.9<><p><em>AG!</em> (Se... | |
| 1 | 1525 | Portugal / Brazil | Sao Tome | Hispaniola, unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | S Maria de Bogoña | Monteiro, Pero | 46473 | [u'ANTT,CC,Parte II, maco 131, doc 54<><i>Inst... |
| 2 | 1526 | Spain / Uruguay | Cape Verde Islands | Cuba, port unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | Carega, Esteban (?) | 11297 | [u'Pike,60-1,172<>Pike, Ruth, <i>Enterprise</i... | |
| 3 | 1526 | Spain / Uruguay | Cape Verde Islands | Cuba, port unspecified | NaN | NaN | NaN | 359.0 | 287.0 | NaN | Carega, Esteban (?) | 11298 | [u'Pike,60-1,172<>Pike, Ruth, <i>Enterprise</i... | |
| 4 | 1526 | NaN | Cape Verde Islands | Caribbean (colony unspecified) | NaN | NaN | NaN | 359.0 | 287.0 | NaN | S Anton | Leon, Juan de | 42631 | [u'Chaunus, 3: 162-63<><p>Chaunus, <em>xxxxxx<... |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20736 | 1864 | Spain / Uruguay | Africa., port unspecified | Cuba, port unspecified | NaN | NaN | NaN | 488.0 | 465.0 | NaN | Polaca | NaN | 46554 | [u'AHNM, Ultramar, Leg. 3551, 6<><i>Archivo Hi... |
| 20737 | 1865 | Spain / Uruguay | Africa., port unspecified | Isla de Pinas | NaN | NaN | NaN | 152.0 | 145.0 | Slave insurrection | Gato | NaN | 4394 | [u'IUP,ST,50/B/137<>Great Britain, <i>Irish Un... |
| 20738 | 1865 | NaN | Africa., port unspecified | Mariel | NaN | NaN | NaN | 780.0 | 650.0 | NaN | NaN | 4395 | [u'IUP,ST,50/B/144<>Great Britain, <i>Irish Un... | |
| 20739 | 1865 | NaN | Congo River | Cuba, port unspecified | NaN | NaN | NaN | 1265.0 | 1004.0 | NaN | Cicerón | Mesquita | 5052 | [u'IUP,ST,50/A/23-4<>Great Britain, <i>Irish U... |
| 20740 | 1866 | NaN | Africa., port unspecified | Cuba, port unspecified | NaN | NaN | NaN | 851.0 | 700.0 | NaN | NaN | 4998 | [u'IUP,ST,50/B/220<>Great Britain, <i>Irish Un... |
20741 rows × 14 columns
Renaming the “flag” column as above will only momentarily change that column’s name, however. If we display our DataFrame, we will see that the column name has not changed permamently.
slave_voyages_df.head(1)
| year_of_arrival | flag | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | sources | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1520 | NaN | Portuguese Guinea | San Juan | NaN | NaN | NaN | 324.0 | 259.0 | NaN | NaN | 42987 | [u'AGI,Patronato 175, r.9<><p><em>AG!</em> (Se... |
To save changes in the DataFrame, we need to reassign the DataFrame to the same variable.
slave_voyages_df = slave_voyages_df.rename(columns={'flag': 'national_affiliation'})
slave_voyages_df.head(1)
| year_of_arrival | national_affiliation | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | sources | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1520 | NaN | Portuguese Guinea | San Juan | NaN | NaN | NaN | 324.0 | 259.0 | NaN | NaN | 42987 | [u'AGI,Patronato 175, r.9<><p><em>AG!</em> (Se... |
Drop Columns#
We can remove a column from the DataFrame with the .drop() method and the column name.
slave_voyages_df = slave_voyages_df.drop(columns="sources")
slave_voyages_df.columns
Index(['year_of_arrival', 'flag', 'place_of_purchase', 'place_of_landing',
'percent_women', 'percent_children', 'percent_men', 'total_embarked',
'total_disembarked', 'resistance_label', 'vessel_name',
'captain's_name', 'voyage_id'],
dtype='object')
Add Columns#
To add a column, we simply put a new column name in square brackets and set it equal to whatever we want the new column to be.
For example, if we wanted to create new columns for the total women and men aboard each voyage, we could set them equal to the product of the “total_disembarked” column * the “percent_women” / “percent_men” columns.
slave_voyages_df['total_women'] = slave_voyages_df['total_embarked'] * slave_voyages_df['percent_women']
slave_voyages_df['total_men'] = slave_voyages_df['total_embarked'] * slave_voyages_df['percent_men']
If we scroll all the way to the right side of the DataFrame, we can see that these columns have been added.
slave_voyages_df.head(1)
| year_of_arrival | national_affiliation | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | total_women | total_men | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 1520 | NaN | Portuguese Guinea | San Juan | NaN | NaN | NaN | 324.0 | 259.0 | NaN | NaN | 42987 | NaN | NaN |
Sort Columns#
We can sort a DataFrame with the .sort_values() method, inside of which we include the parameter by= and indicate the name of the column we want to sort by (written in quotation marks).
For example, we can sort the DataFrame by the voyages that had the largest proportion of enslaved women aboard.
slave_voyages_df.sort_values(by='percent_women', ascending=False)
| year_of_arrival | national_affiliation | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | total_women | total_men | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 19042 | 1819 | Spain / Uruguay | Luanda | Havana | 1.00000 | 0.00000 | 0.00000 | 82.0 | 73.0 | NaN | S José Diligente | Estepa | 14378 | 82.00000 | 0.00000 |
| 4269 | 1715 | France | Senegambia and offshore Atlantic, port unspeci... | Cap Francais | 0.74747 | 0.16162 | 0.09091 | 120.0 | 118.0 | NaN | Elisabeth | Beliveau | 31883 | 89.69640 | 10.90920 |
| 1741 | 1659 | Netherlands | Bonny | Jamaica, port unspecified | 0.72500 | 0.05882 | 0.27500 | 195.0 | 85.0 | NaN | S Jan | Veer, Adriaen Blaes van der | 21566 | 141.37500 | 53.62500 |
| 19047 | 1819 | Spain / Uruguay | Africa., port unspecified | Havana | 0.71429 | 0.00000 | 0.28571 | 178.0 | 161.0 | NaN | Descubridor | Benoit<br/> Valdes | 14868 | 127.14362 | 50.85638 |
| 18950 | 1818 | Spain / Uruguay | Africa., port unspecified | Havana | 0.69355 | 0.30645 | 0.00000 | 69.0 | 62.0 | NaN | Vencedor | Iturrondo | 14857 | 47.85495 | 0.00000 |
| ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
| 20736 | 1864 | Spain / Uruguay | Africa., port unspecified | Cuba, port unspecified | NaN | NaN | NaN | 488.0 | 465.0 | NaN | Polaca | NaN | 46554 | NaN | NaN |
| 20737 | 1865 | Spain / Uruguay | Africa., port unspecified | Isla de Pinas | NaN | NaN | NaN | 152.0 | 145.0 | Slave insurrection | Gato | NaN | 4394 | NaN | NaN |
| 20738 | 1865 | NaN | Africa., port unspecified | Mariel | NaN | NaN | NaN | 780.0 | 650.0 | NaN | NaN | 4395 | NaN | NaN | |
| 20739 | 1865 | NaN | Congo River | Cuba, port unspecified | NaN | NaN | NaN | 1265.0 | 1004.0 | NaN | Cicerón | Mesquita | 5052 | NaN | NaN |
| 20740 | 1866 | NaN | Africa., port unspecified | Cuba, port unspecified | NaN | NaN | NaN | 851.0 | 700.0 | NaN | NaN | 4998 | NaN | NaN |
20741 rows × 15 columns
By default, Pandas will sort in “ascending” order, from the smallest value to the largest value. If we want to sort the largest values first, we need to include another parameter ascending=False.
Because the DataFrame is truncated when it has more than 100 rows, we can use a Python list slice to view the top 30 (or any number less than 100) voyages with enslaved women aboard.
slave_voyages_df.sort_values(by='percent_women', ascending=False)[:30]
| year_of_arrival | national_affiliation | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | total_women | total_men | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 19042 | 1819 | Spain / Uruguay | Luanda | Havana | 1.00000 | 0.00000 | 0.00000 | 82.0 | 73.0 | NaN | S José Diligente | Estepa | 14378 | 82.00000 | 0.00000 |
| 4269 | 1715 | France | Senegambia and offshore Atlantic, port unspeci... | Cap Francais | 0.74747 | 0.16162 | 0.09091 | 120.0 | 118.0 | NaN | Elisabeth | Beliveau | 31883 | 89.69640 | 10.90920 |
| 1741 | 1659 | Netherlands | Bonny | Jamaica, port unspecified | 0.72500 | 0.05882 | 0.27500 | 195.0 | 85.0 | NaN | S Jan | Veer, Adriaen Blaes van der | 21566 | 141.37500 | 53.62500 |
| 19047 | 1819 | Spain / Uruguay | Africa., port unspecified | Havana | 0.71429 | 0.00000 | 0.28571 | 178.0 | 161.0 | NaN | Descubridor | Benoit<br/> Valdes | 14868 | 127.14362 | 50.85638 |
| 18950 | 1818 | Spain / Uruguay | Africa., port unspecified | Havana | 0.69355 | 0.30645 | 0.00000 | 69.0 | 62.0 | NaN | Vencedor | Iturrondo | 14857 | 47.85495 | 0.00000 |
| 19037 | 1819 | Spain / Uruguay | Ambriz | Havana | 0.68456 | 0.00000 | 0.31544 | 164.0 | 149.0 | NaN | Supremo Habanero | Blanco, Pedro | 14372 | 112.26784 | 51.73216 |
| 4894 | 1721 | France | Benin | Martinique, port unspecified | 0.67797 | 0.15254 | 0.16949 | 69.0 | 59.0 | NaN | Saint Jean Triomphant | Gaulart, J | 33087 | 46.77993 | 11.69481 |
| 4150 | 1714 | Great Britain | Calabar | Saint John (Antigua) | 0.66667 | 0.16667 | 0.33333 | 360.0 | 240.0 | NaN | Florida | Payne, Samuel | 75489 | 240.00120 | 119.99880 |
| 6259 | 1734 | Netherlands | Jacquin | Suriname | 0.66480 | 0.09078 | 0.24441 | 716.0 | 374.0 | NaN | Rusthof | Bovenmans, Anthony<br/> Theunissen, Willem | 10278 | 475.99680 | 174.99756 |
| 1823 | 1664 | Great Britain | New Calabar | Barbados, port unspecified | 0.65217 | 0.08696 | 0.26087 | 242.0 | 168.0 | NaN | Elizabeth | Francis, William | 9558 | 157.82514 | 63.13054 |
| 1799 | 1663 | Great Britain | Ardra | Barbados, port unspecified | 0.64667 | 0.00000 | 0.35333 | 373.0 | 150.0 | NaN | Blackamore | Bowles | 9552 | 241.20791 | 131.79209 |
| 15471 | 1792 | Great Britain | Calabar | Grenada, port unspecified | 0.64646 | 0.06965 | 0.29798 | 200.0 | 135.0 | NaN | Fame | Jenkins, William | 18138 | 129.29200 | 59.59600 |
| 1824 | 1664 | Great Britain | Bight of Biafra and Gulf of Guinea Islands, po... | Jamaica, port unspecified | 0.64324 | 0.12973 | 0.22703 | 267.0 | 185.0 | NaN | Allepine | Thwaites, William | 9559 | 171.74508 | 60.61701 |
| 5049 | 1723 | Great Britain | Cabinda | Jamaica, port unspecified | 0.64141 | 0.26768 | 0.09091 | 201.0 | 198.0 | NaN | Royal African Packet | Cornwell, Henry | 76460 | 128.92341 | 18.27291 |
| 2151 | 1677 | Great Britain | Calabar | Barbados, port unspecified | 0.63816 | 0.03289 | 0.32895 | 219.0 | 152.0 | NaN | Lenox | Wilkins, Amos | 9980 | 139.75704 | 72.04005 |
| 17618 | 1804 | Spain / Uruguay | Africa., port unspecified | Buenos Aires | 0.63333 | 0.05000 | 0.31667 | 160.0 | 81.0 | NaN | S Ana | Amaro, Rodriguez de Oliveira | 96099 | 101.33280 | 50.66720 |
| 19040 | 1819 | Spain / Uruguay | Luanda | Havana | 0.62201 | 0.00000 | 0.37799 | 219.0 | 209.0 | NaN | Clarita | Carballo, Alberto | 14375 | 136.22019 | 82.77981 |
| 2318 | 1681 | Great Britain | Gold Coast, port unspecified | Jamaica, port unspecified | 0.62048 | 0.07831 | 0.30120 | 400.0 | 170.0 | NaN | African | Mingham, John | 9918 | 248.19200 | 120.48000 |
| 4364 | 1716 | France | Goree | Leogane | 0.62025 | 0.20736 | 0.37975 | 411.0 | 402.0 | NaN | Duc d'Orléans | Villers, Chevalier de | 31886 | 254.92275 | 156.07725 |
| 5259 | 1725 | France | Senegambia and offshore Atlantic, port unspeci... | Cap Francais | 0.61353 | 0.05314 | 0.33333 | 210.0 | 207.0 | NaN | Prothée | Hantier, J | 32873 | 128.84130 | 69.99930 |
| 4773 | 1720 | France | Formosa | Martinique, port unspecified | 0.61207 | 0.25862 | 0.12931 | 215.0 | 176.0 | Slave insurrection | Don de Dieu | Masson Du Bocage, Guillaume | 33119 | 131.59505 | 27.80165 |
| 2591 | 1686 | Great Britain | New Calabar | Newcastle (Nevis) | 0.60656 | 0.14754 | 0.24590 | 267.0 | 111.0 | NaN | Francis | Wimple, Ralph | 9838 | 161.95152 | 65.65530 |
| 2182 | 1678 | Great Britain | Calabar | Jamaica, port unspecified | 0.60526 | 0.04605 | 0.34868 | 219.0 | 152.0 | NaN | Vine | Coleman, John | 9987 | 132.55194 | 76.36092 |
| 5543 | 1727 | Great Britain | Africa., port unspecified | Rappahannock | 0.60440 | 0.35000 | 0.39560 | 161.0 | 140.0 | NaN | John and Betty | Denton, William | 92352 | 97.30840 | 63.69160 |
| 2540 | 1685 | Great Britain | Cape Coast Castle | Jamaica, port unspecified | 0.59917 | 0.00000 | 0.40083 | 310.0 | 242.0 | NaN | Jefferie | Daile, Benjamin | 9669 | 185.74270 | 124.25730 |
| 4736 | 1719 | France | Africa., port unspecified | Cayenne | 0.59524 | 0.16667 | 0.23810 | 52.0 | 42.0 | NaN | Fidèle | Bonneau, Jean | 30111 | 30.95248 | 12.38120 |
| 2327 | 1681 | Great Britain | New Calabar | Barbados, port unspecified | 0.59231 | 0.06923 | 0.33846 | 232.0 | 130.0 | NaN | Hope | Grible, John | 9927 | 137.41592 | 78.52272 |
| 2377 | 1682 | Great Britain | Calabar | Jamaica, port unspecified | 0.58879 | 0.08411 | 0.32710 | 164.0 | 114.0 | NaN | Arthur | Penny, George | 9898 | 96.56156 | 53.64440 |
| 2376 | 1682 | Great Britain | Calabar | Newcastle (Nevis) | 0.58696 | 0.11957 | 0.29348 | 146.0 | 119.0 | NaN | Eaglet | Waugh, John | 9896 | 85.69616 | 42.84808 |
| 1833 | 1664 | Great Britain | Calabar | Barbados, port unspecified | 0.58549 | 0.15544 | 0.25907 | 278.0 | 193.0 | NaN | Success | Duke, Abraham | 9584 | 162.76622 | 72.02146 |
If we want to sort a Series object, we don’t need to use the by= paramter.
slave_voyages_df['total_women'].sort_values(ascending=False)
6259 475.996800
18822 316.640164
2264 311.833480
6163 303.000180
3129 301.764000
...
20736 NaN
20737 NaN
20738 NaN
20739 NaN
20740 NaN
Name: total_women, Length: 20741, dtype: float64
Calculate Columns#
We can do different calculations on columns with built-in Pandas functions. These calculations will ignore NaN values.
Pandas calculations |
Explanation |
|---|---|
|
Number of observations |
|
Sum of values |
|
Mean of values |
|
Median of values |
|
Minimum |
|
Maximum |
|
Mode |
|
Unbiased standard deviation |
For example, to find the average proprotion of enslaved women aboard the voyages (for voyages that have this information), we can use the .mean() method.
slave_voyages_df['percent_women'].mean()
0.27419760653973735
There were on average 27% enslaved women aboard the voyages for voyages that recorded this information.
slave_voyages_df['percent_women'].max()
1.0
The highest percentage of women aboard the slave voyages was 100%. We can use this calculation as a filter to identify the voyage(s) with this maximum value.
slave_voyages_df[slave_voyages_df['percent_women'] == slave_voyages_df['percent_women'].max()]
| year_of_arrival | national_affiliation | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | total_women | total_men | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 19042 | 1819 | Spain / Uruguay | Luanda | Havana | 1.0 | 0.0 | 0.0 | 82.0 | 73.0 | NaN | S José Diligente | Estepa | 14378 | 82.0 | 0.0 |
According to the Trans-Atlantic Slave Trade Database, the 1819 voyage of the S José Diligente had 100% enslaved women aboard.
As demonstrated previously, we can also perform calculations with columns themselves.
(slave_voyages_df['total_embarked'] * slave_voyages_df['percent_women']).max()
475.99679999999995
Groupby Columns#
The Pandas function.groupby() allows us to group data and perform calculations on the groups.
For example, Jennifer Morgan writes about how some nations recorded more information about the gender of the enslaved people aboard their voyages than other nations did. To see the breakdown of gender information by nation, we can use a .groupby() function.
The first step to using groupby is to type the name of the DataFrame followed by .groupby() with the column we’d like to aggregate based on, such as “national_affiliation.”
slave_voyages_df.groupby('national_affiliation')
<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7fdd2d0c2150>
This action will created a GroupBy object. We can perform calculations on this grouped data, such as counting the number of non-blank values in each column for each nation.
slave_voyages_df.groupby('national_affiliation').count()
| year_of_arrival | place_of_purchase | place_of_landing | percent_women | percent_children | percent_men | total_embarked | total_disembarked | resistance_label | vessel_name | captain's_name | voyage_id | total_women | total_men | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| national_affiliation | ||||||||||||||
| Denmark / Baltic | 290 | 290 | 290 | 119 | 119 | 119 | 290 | 290 | 8 | 290 | 163 | 290 | 119 | 119 |
| France | 3381 | 3377 | 3381 | 708 | 709 | 708 | 3381 | 3381 | 121 | 3381 | 3289 | 3381 | 708 | 708 |
| Great Britain | 10536 | 10530 | 10536 | 1123 | 1151 | 1123 | 10526 | 10525 | 152 | 10536 | 10226 | 10536 | 1123 | 1123 |
| Netherlands | 1389 | 1341 | 1389 | 200 | 201 | 200 | 1387 | 1387 | 51 | 1389 | 1316 | 1389 | 200 | 200 |
| Other | 4 | 4 | 4 | 0 | 0 | 0 | 4 | 4 | 0 | 4 | 2 | 4 | 0 | 0 |
| Portugal / Brazil | 1009 | 1009 | 1009 | 42 | 48 | 42 | 1009 | 1009 | 0 | 1009 | 874 | 1009 | 42 | 42 |
| Spain / Uruguay | 1528 | 1524 | 1528 | 468 | 465 | 468 | 1528 | 1528 | 4 | 1528 | 1323 | 1528 | 468 | 468 |
| U.S.A. | 1446 | 1436 | 1446 | 223 | 223 | 223 | 1443 | 1442 | 31 | 1446 | 1283 | 1446 | 223 | 223 |
We can also isolate only the “percent_women” column.
slave_voyages_df.groupby('national_affiliation').count()['percent_women']
national_affiliation
Denmark / Baltic 119
France 708
Great Britain 1123
Netherlands 200
Other 0
Portugal / Brazil 42
Spain / Uruguay 468
U.S.A. 223
Name: percent_women, dtype: int64
slave_voyages_df.groupby('national_affiliation')['percent_women'].count().sort_values(ascending=False)
national_affiliation
Great Britain 1123
France 708
Spain / Uruguay 468
U.S.A. 223
Netherlands 200
Denmark / Baltic 119
Portugal / Brazil 42
Other 0
Name: percent_women, dtype: int64
slave_voyages_df.groupby('national_affiliation')['percent_women'].count()\
.sort_values(ascending=False).plot(kind='bar', title='Trans-Atlantic Slave Trade (Americas): \n Slave Voyages with Recorded Gender Information')
<matplotlib.axes._subplots.AxesSubplot at 0x7fdd2bda9750>
Make Time Series with Groupby#
To make a time series, we would typically want to convert our date column into datetime values rather than integers.
slave_voyages_df['year_of_arrival'].dtype
dtype('int64')
Datetime values allow us to do special things that we can’t do with regular integers and floats, such as extract just the year, month, week, day, or second from any date or aggregate based on any of the above.
However, we can also make some simple time series plots just by grouping by the year column and performing calculations on those year groupings, such as calculating the average percentage of enslaved women aboard the voyages over time.
slave_voyages_df.groupby('year_of_arrival')['total_women'].sum()
year_of_arrival
1520 0.0
1525 0.0
1526 0.0
1527 0.0
1532 0.0
...
1862 0.0
1863 0.0
1864 0.0
1865 0.0
1866 0.0
Name: total_women, Length: 330, dtype: float64
total_women_by_year = slave_voyages_df.groupby('year_of_arrival')['total_women'].sum()
total_women_by_year.plot()
<matplotlib.axes._subplots.AxesSubplot at 0x7fbc383adb50>
total_women_by_year.plot(kind='line', title="Trans-Atlantic Slave Trade (Americas):\nTotal Number of Enslaved Women on Voyages")
<matplotlib.axes._subplots.AxesSubplot at 0x7fbc39cfa110>
We can put different plots on the same axes by assigning one of the plots to the variable ax, short for axes, and then using ax=ax in the other plot to explicitly put it on the same axes.
total_men_by_year = slave_voyages_df.groupby('year_of_arrival')['total_men'].sum()
ax = total_women_by_year.plot(kind='line', legend= True,
title="Trans-Atlantic Slave Trade (Americas):\nTotal Number of Enslaved Women on Voyages")
total_men_by_year.plot(ax=ax, legend=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7fbc39ec9c90>
We can change the labels in a legend by using the label= parameter.
ax = total_women_by_year.plot(kind='line', label="Total Women", legend= True, title="Trans-Atlantic Slave Trade (Americas):\nTotal Number of Enslaved Women on Voyages")
total_men_by_year.plot(ax=ax, label="Total Men", legend=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7fbc3a004710>
Finally, we can also add in the total number of enslaved people who embarked on the voyages, offering a perspective of how much gender information we have about the voyages compared to the total number of voyages.
total_embarked_by_year = slave_voyages_df.groupby('year_of_arrival')['total_embarked'].sum()
ax = total_women_by_year.plot(kind='line', label="Total Women", legend= True, title="Trans-Atlantic Slave Trade (Americas):\nTotal Number of Enslaved Women on Voyages")
total_men_by_year.plot(ax=ax, label="Total Men", legend=True)
total_embarked_by_year.plot(ax=ax, label="Total Embarked", legend=True)
<matplotlib.axes._subplots.AxesSubplot at 0x7fbc3a0f8f10>
Save Plots#
To save a plot as an image file or PDF file, we can again assign the plot to a variable called ax, short for axes.
Then we can use ax.figure.savefig('FILE-NAME.png') or ax.figure.savefig('FILE-NAME.pdf').
ax = total_women_by_year.plot(kind='line', label="Total Women", legend= True, title="Trans-Atlantic Slave Trade (Americas):\nTotal Number of Enslaved Women on Voyages")
total_men_by_year.plot(ax=ax, label="Total Men", legend=True)
total_embarked_by_year.plot(ax=ax, label="Total Embarked", legend=True)
ax.figure.savefig('Trans-Atlantic-Slave-Trade_Gender-Info.png')
Prevent Labels From Getting Cut Off#
If labels are getting cut off in your image, you can explicitly import matplotlib.pyplot (the data viz Python library that Pandas .plot()s are built on) and use the tight_layout() function:
import matplotlib.pyplot as plt
ax = total_women_by_year.plot(kind='line', label="Total Women", legend= True, title="Trans-Atlantic Slave Trade (Americas):\nTotal Number of Enslaved Women on Voyages")
total_men_by_year.plot(ax=ax, label="Total Men", legend=True)
total_embarked_by_year.plot(ax=ax, label="Total Embarked", legend=True)
plt.tight_layout()
ax.figure.savefig('Trans-Atlantic-Slave-Trade_Gender-Info.png')