# 2.1. Finding Data in DataFrame#

## 2.1.1. About the Titanic Dataset#

import pandas as pd


Throughout this chapter, we will be using the same dataset, the infamous Titanic Survivor dataset that was put out by Kaggle. It has since become a staple dataset in machine learning and data science communities because of the breadth of data that it offers on the Titanic passengers. The goal of most machine learning challenges is to use this dataset to train a model that can accurately predict if a passenger would have survived given certain gender, economic, and class conditions.

In this section, we will be using a cleaned version of the dataset available on the GitHub page below. We will be using it because of its breadth of its qualitative and quantitative data.

In this notebook, we are strictly interested in finding key data in the DataFrame. By the end of the notebook, you should have a good understanding of why working with CSV data in Python via Pandas is far more powerful and easy than using Excel.

#data obtained from => https://github.com/datasciencedojo/datasets


## 2.1.2. How to Find Column Data#

Let’s presume that we just obtained this dataset. Let’s also presume we know nothing about it. Our first job is to get a sense of the data. Maybe we want to know all the columns that the dataset contains. To find this information, we can use df.columns

df.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
dtype='object')


With our knowledge from the last chapter, we now know how to convert this into a list!

cols = df.columns.tolist()

cols

['PassengerId',
'Survived',
'Pclass',
'Name',
'Sex',
'Age',
'SibSp',
'Parch',
'Ticket',
'Fare',
'Cabin',
'Embarked']


## 2.1.3. How to Get a Quick Sense of the Dataset with df.head()#

Another way to get a quick sense of the data is to use df.head(). This allows us to return to top pieces of information from the dataset. By default, head returns the top 5 rows.

df.head()

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S

We can pass in an argument to return more than 5. Let’s try and display 20.

df.head(20)

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 0 373450 8.0500 NaN S
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S
7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7000 G6 S
11 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.5500 C103 S
12 13 0 3 Saundercock, Mr. William Henry male 20.0 0 0 A/5. 2151 8.0500 NaN S
13 14 0 3 Andersson, Mr. Anders Johan male 39.0 1 5 347082 31.2750 NaN S
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 0 350406 7.8542 NaN S
15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 0 248706 16.0000 NaN S
16 17 0 3 Rice, Master. Eugene male 2.0 4 1 382652 29.1250 NaN Q
17 18 1 2 Williams, Mr. Charles Eugene male NaN 0 0 244373 13.0000 NaN S
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 0 345763 18.0000 NaN S
19 20 1 3 Masselmani, Mrs. Fatima female NaN 0 0 2649 7.2250 NaN C

## 2.1.4. How to Grab a Specific Range of Rows with df.iloc[]#

Sometimes, however, you need to grab a specific range of rows. Let’s say I am interested in rows 5-20. To grab this data, we can use the df.iloc[] command that we met in the last notebook. This will allow us to pass a specific index range like a list.

df.iloc[5:20]

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
5 6 0 3 Moran, Mr. James male NaN 0 0 330877 8.4583 NaN Q
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S
7 8 0 3 Palsson, Master. Gosta Leonard male 2.0 3 1 349909 21.0750 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7000 G6 S
11 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.5500 C103 S
12 13 0 3 Saundercock, Mr. William Henry male 20.0 0 0 A/5. 2151 8.0500 NaN S
13 14 0 3 Andersson, Mr. Anders Johan male 39.0 1 5 347082 31.2750 NaN S
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 0 350406 7.8542 NaN S
15 16 1 2 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 0 248706 16.0000 NaN S
16 17 0 3 Rice, Master. Eugene male 2.0 4 1 382652 29.1250 NaN Q
17 18 1 2 Williams, Mr. Charles Eugene male NaN 0 0 244373 13.0000 NaN S
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 0 345763 18.0000 NaN S
19 20 1 3 Masselmani, Mrs. Fatima female NaN 0 0 2649 7.2250 NaN C

## 2.1.5. How to Get a Quick Quantitative Understanding of the Dataset with Describe()#

When working with numerical, quantitative data, we can automatically grab all numerical rows and learn a lot about the data with df.describe(). This will return the count, mean, standard deviation, minimum, maximum, etc. of our quantitative data. Sometimes, this data is useful to compute in such a way, such as the column Survived. Here, we can see that roughly .38 or 38% of the passengers (in this dataset) survived. Other numerical data does not really lend itself well to this kind of analysis, e.g. PassengerId which a unique numerical number corresponding to each passenger.

df.describe()

PassengerId Survived Pclass Age SibSp Parch Fare
count 891.000000 891.000000 891.000000 714.000000 891.000000 891.000000 891.000000
mean 446.000000 0.383838 2.308642 29.699118 0.523008 0.381594 32.204208
std 257.353842 0.486592 0.836071 14.526497 1.102743 0.806057 49.693429
min 1.000000 0.000000 1.000000 0.420000 0.000000 0.000000 0.000000
25% 223.500000 0.000000 2.000000 20.125000 0.000000 0.000000 7.910400
50% 446.000000 0.000000 3.000000 28.000000 0.000000 0.000000 14.454200
75% 668.500000 1.000000 3.000000 38.000000 1.000000 0.000000 31.000000
max 891.000000 1.000000 3.000000 80.000000 8.000000 6.000000 512.329200

## 2.1.6. How to Find Specific Information in the Dataset with df.loc#

As researchers, however, we often need to find targeted information in our dataset. Let’s say I am researching female passengers on the Titanic. I can achieve this fairly easily in Excel and in Python with Pandas. To do this in Pandas, you can use df.loc[] to pass a specific argument. In the example below, we are stating that we are looking for the columns in the DataFrame of “Sex” that match the string “female”. Try to recreate this and find all males in the dataset on your own. Note at the bottom of the DataFrame 314x12. This is the dimensions of the DataFrame. We can see that we have 314 results.

df.loc[df["Sex"] == "female"]

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
... ... ... ... ... ... ... ... ... ... ... ... ...
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 1 230433 26.0000 NaN S
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.0 0 0 7552 10.5167 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S

314 rows × 12 columns

But that’s not all df.loc[] can do. We can add multiple arguments to our search query here, rather like SQL. In the example below, note the addition of the parentheses around df["Sex"] == "female" this denotes one parameter of our search. We then use the & to add a second parameter. In this case, we are interested in not only returning those that are female, but also those who are in Pclass, or Passenger Class 1, i.e. First Class. When we execute the command below, we see that we have found 94 results.

df.loc[(df["Sex"] == "female") & (df["Pclass"] == 1)]

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
11 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.5500 C103 S
31 32 1 1 Spencer, Mrs. William Augustus (Marie Eugenie) female NaN 1 0 PC 17569 146.5208 B78 C
52 53 1 1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.0 1 0 PC 17572 76.7292 D33 C
... ... ... ... ... ... ... ... ... ... ... ... ...
856 857 1 1 Wick, Mrs. George Dennick (Mary Hitchcock) female 45.0 1 1 36928 164.8667 NaN S
862 863 1 1 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 0 17466 25.9292 D17 S
871 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 1 11751 52.5542 D35 S
879 880 1 1 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 1 11767 83.1583 C50 C
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S

94 rows × 12 columns

Let’s say now we are interested in only finding those that did not survive of the 94. To do that, we would simply add a third argument to our query. We ask Pandas to only show those whose Survived value is 0, or False. In other words, we are seeking to find those who did not survive.

df.loc[(df["Sex"] == "female") & (df["Pclass"] == 1) & (df["Survived"] == 0)]

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
177 178 0 1 Isham, Miss. Ann Elizabeth female 50.0 0 0 PC 17595 28.7125 C49 C
297 298 0 1 Allison, Miss. Helen Loraine female 2.0 1 2 113781 151.5500 C22 C26 S
498 499 0 1 Allison, Mrs. Hudson J C (Bessie Waldo Daniels) female 25.0 1 2 113781 151.5500 C22 C26 S

With this data, I not only have qualitative information about each passenger, I also have some significant quantitative information as well. I know that of the 314 people in the dataset with the sex of female, 94 were in first class and of those 94, all survived except 3. Let’s now pose a question and I think you may already know the answer. Did class in any way play a role in the chance off survival on the Titanic. We don’t need a fancy machine learning algorithm to help us answer this question. We can simply examine the results from our query. Let’s find the total number of passengers that are identified as female, but not in first class, and then find the number of those who did not survive.

df.loc[(df["Sex"] == "female") & (df["Pclass"] > 1)]

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7000 G6 S
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 0 350406 7.8542 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
875 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15.0 0 0 2667 7.2250 NaN C
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 1 230433 26.0000 NaN S
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.0 0 0 7552 10.5167 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S

220 rows × 12 columns

df.loc[(df["Sex"] == "female") & (df["Pclass"] > 1) & (df["Survived"] == 0)]

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 0 350406 7.8542 NaN S
18 19 0 3 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 0 345763 18.0000 NaN S
24 25 0 3 Palsson, Miss. Torborg Danira female 8.0 3 1 349909 21.0750 NaN S
38 39 0 3 Vander Planke, Miss. Augusta Maria female 18.0 2 0 345764 18.0000 NaN S
40 41 0 3 Ahlin, Mrs. Johan (Johanna Persdotter Larsson) female 40.0 1 0 7546 9.4750 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
854 855 0 2 Carter, Mrs. Ernest Courtenay (Lilian Hughes) female 44.0 1 0 244252 26.0000 NaN S
863 864 0 3 Sage, Miss. Dorothy Edith "Dolly" female NaN 8 2 CA. 2343 69.5500 NaN S
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.0 0 0 7552 10.5167 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S

78 rows × 12 columns

As we can see, we have 220 total people identified as female with 78 not surviving. We can perform some quick calculations to understand better our results.

x = 3/94
x

0.031914893617021274

y = 78/220
y

0.35454545454545455


In this case, x is the death rate for a first class female, whereas y is the death rate for those not in first class and female. The result is clear. The chance for survival was significantly greater for those identified as female if they were in first class, roughly 32.5% difference. Were there other factors at play, possibly. But that is beyond the point here. I am simply trying to illustrate how to obtain data to begin framing the research question at hand.

Although we can achieve this same analysis in Excel, doing this in Python allows us to leverage the power of an entire programming language with the data at hand. This is a simple example of the power of Pandas over Excel. As we move forward in the next sections, you will see more examples of this.

## 2.1.7. How to Query with “OR” (|) on a DataFrame#

Above we saw how to use df.loc to structure logical arguments for finding two specific conditions with “&”. This indicated that both conditions must be true to be returned as a result. In case you don’t remember it, it looked like this:

df.loc[(df["Sex"] == "female") & (df["Pclass"] > 1)]

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
8 9 1 3 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 2 347742 11.1333 NaN S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
10 11 1 3 Sandstrom, Miss. Marguerite Rut female 4.0 1 1 PP 9549 16.7000 G6 S
14 15 0 3 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 0 350406 7.8542 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
875 876 1 3 Najib, Miss. Adele Kiamie "Jane" female 15.0 0 0 2667 7.2250 NaN C
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 1 230433 26.0000 NaN S
882 883 0 3 Dahlberg, Miss. Gerda Ulrika female 22.0 0 0 7552 10.5167 NaN S
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S

220 rows × 12 columns

We can do the same conditional approach, but rather than using and, we can also tell Pandas “Or” with the |; this is known as a pipe. Let’s try and grab all first and second class passengers. We can specify if the Pclass is either 1 OR 2.

df.loc[(df["Pclass"] == 1) | (df["Pclass"] == 2)]

PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 0 PC 17599 71.2833 C85 C
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 0 113803 53.1000 C123 S
6 7 0 1 McCarthy, Mr. Timothy J male 54.0 0 0 17463 51.8625 E46 S
9 10 1 2 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 0 237736 30.0708 NaN C
11 12 1 1 Bonnell, Miss. Elizabeth female 58.0 0 0 113783 26.5500 C103 S
... ... ... ... ... ... ... ... ... ... ... ... ...
880 881 1 2 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 1 230433 26.0000 NaN S
883 884 0 2 Banfield, Mr. Frederick James male 28.0 0 0 C.A./SOTON 34068 10.5000 NaN S
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.0000 B42 S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C

400 rows × 12 columns

By learning to use Pandas in this way and structuring Pandas-based queries, we can generate the data necessary to begin answering research questions about our data. Pandas can do more, however; it can also let us organize our data.