Data Processing¶

This tutorial explains how tabular data can be handled and transformed with the Table class.

Note

All operations on a Table return a new Table. The original Table will not be changed.

Create & Load data¶

1. Load your data into a Table:

In [1]:

from safeds.data.tabular.containers import Table

titanic = Table.from_csv_file("data/titanic.csv")

from safeds.data.tabular.containers import Table titanic = Table.from_csv_file("data/titanic.csv")

2. Create a Table containing only the first 10 rows:

In [2]:

titanic_slice = titanic.slice_rows(end=10)

titanic_slice # just to show the output

titanic_slice = titanic.slice_rows(end=10) titanic_slice # just to show the output

Out[2]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	male	42.0	0	0	C.A. 5547	3	7.5500	NaN	Southampton	0
1	1	Abbott, Master. Eugene Joseph	male	13.0	0	2	C.A. 2673	3	20.2500	NaN	Southampton	0
2	2	Abbott, Mr. Rossmore Edward	male	16.0	1	1	C.A. 2673	3	20.2500	NaN	Southampton	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	35.0	1	1	C.A. 2673	3	20.2500	NaN	Southampton	1
4	4	Abelseth, Miss. Karen Marie	female	16.0	0	0	348125	3	7.6500	NaN	Southampton	1
5	5	Abelseth, Mr. Olaus Jorgensen	male	25.0	0	0	348122	3	7.6500	F G63	Southampton	1
6	6	Abelson, Mr. Samuel	male	30.0	1	0	P/PP 3381	2	24.0000	NaN	Cherbourg	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	2	24.0000	NaN	Cherbourg	1
8	8	Abrahamsson, Mr. Abraham August Johannes	male	20.0	0	0	SOTON/O2 3101284	3	7.9250	NaN	Southampton	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	18.0	0	0	2657	3	7.2292	NaN	Cherbourg	1

3. Extract a Row from your Table:

In [3]:

titanic_slice.get_row(0)

titanic_slice.get_row(0)

Out[3]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	male	42.0	0	0	C.A. 5547	3	7.55	NaN	Southampton	0

4. Extract a Column from your Table:

In [4]:

titanic_slice.get_column("name")

titanic_slice.get_column("name")

Out[4]:

	name
0	Abbing, Mr. Anthony
1	Abbott, Master. Eugene Joseph
2	Abbott, Mr. Rossmore Edward
3	Abbott, Mrs. Stanton (Rosa Hunt)
4	Abelseth, Miss. Karen Marie
5	Abelseth, Mr. Olaus Jorgensen
6	Abelson, Mr. Samuel
7	Abelson, Mrs. Samuel (Hannah Wizosky)
8	Abrahamsson, Mr. Abraham August Johannes
9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)

5. Combine a list of Rows to a Table (make sure the Rows have the same columns):

In [5]:

Table.from_rows([
    titanic_slice.get_row(0),
    titanic_slice.get_row(1)
])

Table.from_rows([ titanic_slice.get_row(0), titanic_slice.get_row(1) ])

Out[5]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	male	42.0	0	0	C.A. 5547	3	7.55	NaN	Southampton	0
1	1	Abbott, Master. Eugene Joseph	male	13.0	0	2	C.A. 2673	3	20.25	NaN	Southampton	0

6. Combine a list of Columns to a Table (make sure the Columns have the same amount of rows):

In [6]:

Table.from_columns([
    titanic_slice.get_column("name"),
    titanic_slice.get_column("age")
])

Table.from_columns([ titanic_slice.get_column("name"), titanic_slice.get_column("age") ])

Out[6]:

	name	age
0	Abbing, Mr. Anthony	42.0
1	Abbott, Master. Eugene Joseph	13.0
2	Abbott, Mr. Rossmore Edward	16.0
3	Abbott, Mrs. Stanton (Rosa Hunt)	35.0
4	Abelseth, Miss. Karen Marie	16.0
5	Abelseth, Mr. Olaus Jorgensen	25.0
6	Abelson, Mr. Samuel	30.0
7	Abelson, Mrs. Samuel (Hannah Wizosky)	28.0
8	Abrahamsson, Mr. Abraham August Johannes	20.0
9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	18.0

7. Drop columns from a Table:

In [7]:

titanic_slice.remove_columns([
    "id",
    "name",
    "ticket",
    "cabin",
    "port_embarked",
    "survived"
])

titanic_slice.remove_columns([ "id", "name", "ticket", "cabin", "port_embarked", "survived" ])

Out[7]:

	sex	age	siblings_spouses	parents_children	travel_class	fare
0	male	42.0	0	0	3	7.5500
1	male	13.0	0	2	3	20.2500
2	male	16.0	1	1	3	20.2500
3	female	35.0	1	1	3	20.2500
4	female	16.0	0	0	3	7.6500
5	male	25.0	0	0	3	7.6500
6	male	30.0	1	0	2	24.0000
7	female	28.0	1	0	2	24.0000
8	male	20.0	0	0	3	7.9250
9	female	18.0	0	0	3	7.2292

8. Keep only specified columns of a Table:

In [8]:

titanic_slice.keep_only_columns(["name", "survived"])

titanic_slice.keep_only_columns(["name", "survived"])

Out[8]:

	name	survived
0	Abbing, Mr. Anthony	0
1	Abbott, Master. Eugene Joseph	0
2	Abbott, Mr. Rossmore Edward	0
3	Abbott, Mrs. Stanton (Rosa Hunt)	1
4	Abelseth, Miss. Karen Marie	1
5	Abelseth, Mr. Olaus Jorgensen	1
6	Abelson, Mr. Samuel	0
7	Abelson, Mrs. Samuel (Hannah Wizosky)	1
8	Abrahamsson, Mr. Abraham August Johannes	1
9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	1

Process data¶

1. Sort columns by their name:

In [9]:

titanic_slice.sort_columns()

titanic_slice.sort_columns()

Out[9]:

	age	cabin	fare	id	name	parents_children	port_embarked	sex	siblings_spouses	survived	ticket	travel_class
0	42.0	NaN	7.5500	0	Abbing, Mr. Anthony	0	Southampton	male	0	0	C.A. 5547	3
1	13.0	NaN	20.2500	1	Abbott, Master. Eugene Joseph	2	Southampton	male	0	0	C.A. 2673	3
2	16.0	NaN	20.2500	2	Abbott, Mr. Rossmore Edward	1	Southampton	male	1	0	C.A. 2673	3
3	35.0	NaN	20.2500	3	Abbott, Mrs. Stanton (Rosa Hunt)	1	Southampton	female	1	1	C.A. 2673	3
4	16.0	NaN	7.6500	4	Abelseth, Miss. Karen Marie	0	Southampton	female	0	1	348125	3
5	25.0	F G63	7.6500	5	Abelseth, Mr. Olaus Jorgensen	0	Southampton	male	0	1	348122	3
6	30.0	NaN	24.0000	6	Abelson, Mr. Samuel	0	Cherbourg	male	1	0	P/PP 3381	2
7	28.0	NaN	24.0000	7	Abelson, Mrs. Samuel (Hannah Wizosky)	0	Cherbourg	female	1	1	P/PP 3381	2
8	20.0	NaN	7.9250	8	Abrahamsson, Mr. Abraham August Johannes	0	Southampton	male	0	1	SOTON/O2 3101284	3
9	18.0	NaN	7.2292	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	0	Cherbourg	female	0	1	2657	3

2. Sort columns with a custom comparator:

In [10]:

titanic_slice.sort_columns(
    lambda column1, column2:
        (column1.name < column2.name) - (column1.name > column2.name)
)

titanic_slice.sort_columns( lambda column1, column2: (column1.name < column2.name) - (column1.name > column2.name) )

Out[10]:

	travel_class	ticket	survived	siblings_spouses	sex	port_embarked	parents_children	name	id	fare	cabin	age
0	3	C.A. 5547	0	0	male	Southampton	0	Abbing, Mr. Anthony	0	7.5500	NaN	42.0
1	3	C.A. 2673	0	0	male	Southampton	2	Abbott, Master. Eugene Joseph	1	20.2500	NaN	13.0
2	3	C.A. 2673	0	1	male	Southampton	1	Abbott, Mr. Rossmore Edward	2	20.2500	NaN	16.0
3	3	C.A. 2673	1	1	female	Southampton	1	Abbott, Mrs. Stanton (Rosa Hunt)	3	20.2500	NaN	35.0
4	3	348125	1	0	female	Southampton	0	Abelseth, Miss. Karen Marie	4	7.6500	NaN	16.0
5	3	348122	1	0	male	Southampton	0	Abelseth, Mr. Olaus Jorgensen	5	7.6500	F G63	25.0
6	2	P/PP 3381	0	1	male	Cherbourg	0	Abelson, Mr. Samuel	6	24.0000	NaN	30.0
7	2	P/PP 3381	1	1	female	Cherbourg	0	Abelson, Mrs. Samuel (Hannah Wizosky)	7	24.0000	NaN	28.0
8	3	SOTON/O2 3101284	1	0	male	Southampton	0	Abrahamsson, Mr. Abraham August Johannes	8	7.9250	NaN	20.0
9	3	2657	1	0	female	Cherbourg	0	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	9	7.2292	NaN	18.0

3. Filter rows with a given query:

In [11]:

titanic.filter_rows(
    lambda row:
        "van" in row.get_value("name")
)

titanic.filter_rows( lambda row: "van" in row.get_value("name") )

/tmp/ipykernel_3461/1773701666.py:1: DeprecationWarning: This method is deprecated and will be removed in a future version. Use `Table.keep_only_rows` instead.
  titanic.filter_rows(

Out[11]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	105	Beavan, Mr. William Thomas	male	19.0	0	0	323951	3	8.0500	NaN	Southampton	0
1	191	Canavan, Miss. Mary	female	21.0	0	0	364846	3	7.7500	NaN	Queenstown	0
2	192	Canavan, Mr. Patrick	male	21.0	0	0	364858	3	7.7500	NaN	Queenstown	0
3	267	Cor, Mr. Ivan	male	27.0	0	0	349229	3	7.8958	NaN	Southampton	0
4	308	Davies, Mr. Evan	male	22.0	0	0	SC/A4 23568	3	8.0500	NaN	Southampton	0
5	333	Devaney, Miss. Margaret Delia	female	19.0	0	0	330958	3	7.8792	NaN	Queenstown	1
6	338	Dimic, Mr. Jovan	male	42.0	0	0	315088	3	8.6625	NaN	Southampton	0
7	383	Evans, Miss. Edith Corse	female	36.0	0	0	PC 17531	1	31.6792	A29	Cherbourg	0
8	586	Ivanoff, Mr. Kanio	male	NaN	0	0	349201	3	7.8958	NaN	Southampton	0
9	589	Jalsevac, Mr. Ivan	male	29.0	0	0	349240	3	7.8958	NaN	Cherbourg	1
10	811	Mineff, Mr. Ivan	male	24.0	0	0	349233	3	7.8958	NaN	Southampton	0
11	916	O'Sullivan, Miss. Bridget Mary	female	NaN	0	0	330909	3	7.6292	NaN	Queenstown	0
12	918	Ovies y Rodriguez, Mr. Servando	male	28.5	0	0	PC 17562	1	27.7208	D43	Cherbourg	0
13	990	Rasmussen, Mrs. (Lena Jacobsen Solvang)	female	NaN	0	0	65305	3	8.1125	NaN	Southampton	0
14	1143	Staneff, Mr. Ivan	male	NaN	0	0	349208	3	7.8958	NaN	Southampton	0
15	1144	Stankovic, Mr. Ivan	male	33.0	0	0	349239	3	8.6625	NaN	Cherbourg	0
16	1161	Strilic, Mr. Ivan	male	27.0	0	0	315083	3	8.6625	NaN	Southampton	0
17	1215	van Billiard, Master. James William	male	NaN	1	1	A/5. 851	3	14.5000	NaN	Southampton	0
18	1216	van Billiard, Master. Walter John	male	11.5	1	1	A/5. 851	3	14.5000	NaN	Southampton	0
19	1217	van Billiard, Mr. Austin Blyler	male	40.5	0	2	A/5. 851	3	14.5000	NaN	Southampton	0
20	1222	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	3	9.5000	NaN	Southampton	0

Transform table¶

1. Transform table using Imputer. Imputers replace missing values with other values (e.g. a constant, the mean or the median of the column etc.) depending on the chosen startegy, for example, the following Imputer will replace missing values in the given columns of the table with the constant 0:

In [12]:

from safeds.data.tabular.transformation import Imputer

imputer = Imputer(Imputer.Strategy.Constant(0)).fit(titanic, ["age", "fare", "cabin", "port_embarked"])
imputer.transform(titanic_slice)

from safeds.data.tabular.transformation import Imputer imputer = Imputer(Imputer.Strategy.Constant(0)).fit(titanic, ["age", "fare", "cabin", "port_embarked"]) imputer.transform(titanic_slice)

Out[12]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	male	42.0	0	0	C.A. 5547	3	7.55	0	Southampton	0
1	1	Abbott, Master. Eugene Joseph	male	13.0	0	2	C.A. 2673	3	20.25	0	Southampton	0
2	2	Abbott, Mr. Rossmore Edward	male	16.0	1	1	C.A. 2673	3	20.25	0	Southampton	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	35.0	1	1	C.A. 2673	3	20.25	0	Southampton	1
4	4	Abelseth, Miss. Karen Marie	female	16.0	0	0	348125	3	7.65	0	Southampton	1
5	5	Abelseth, Mr. Olaus Jorgensen	male	25.0	0	0	348122	3	7.65	F G63	Southampton	1
6	6	Abelson, Mr. Samuel	male	30.0	1	0	P/PP 3381	2	24.0	0	Cherbourg	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	2	24.0	0	Cherbourg	1
8	8	Abrahamsson, Mr. Abraham August Johannes	male	20.0	0	0	SOTON/O2 3101284	3	7.925	0	Southampton	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	18.0	0	0	2657	3	7.2292	0	Cherbourg	1

2. Transform table using LabelEncoder, this will encode categorical features in the chosen Columns as integers:

In [13]:

from safeds.data.tabular.transformation import LabelEncoder

encoder = LabelEncoder().fit(titanic, ["sex", "port_embarked"])
encoder.transform(titanic_slice)

from safeds.data.tabular.transformation import LabelEncoder encoder = LabelEncoder().fit(titanic, ["sex", "port_embarked"]) encoder.transform(titanic_slice)

Out[13]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	1.0	42.0	0	0	C.A. 5547	3	7.5500	NaN	2.0	0
1	1	Abbott, Master. Eugene Joseph	1.0	13.0	0	2	C.A. 2673	3	20.2500	NaN	2.0	0
2	2	Abbott, Mr. Rossmore Edward	1.0	16.0	1	1	C.A. 2673	3	20.2500	NaN	2.0	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	0.0	35.0	1	1	C.A. 2673	3	20.2500	NaN	2.0	1
4	4	Abelseth, Miss. Karen Marie	0.0	16.0	0	0	348125	3	7.6500	NaN	2.0	1
5	5	Abelseth, Mr. Olaus Jorgensen	1.0	25.0	0	0	348122	3	7.6500	F G63	2.0	1
6	6	Abelson, Mr. Samuel	1.0	30.0	1	0	P/PP 3381	2	24.0000	NaN	0.0	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	0.0	28.0	1	0	P/PP 3381	2	24.0000	NaN	0.0	1
8	8	Abrahamsson, Mr. Abraham August Johannes	1.0	20.0	0	0	SOTON/O2 3101284	3	7.9250	NaN	2.0	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	0.0	18.0	0	0	2657	3	7.2292	NaN	0.0	1

3. Transform table using OneHotEncoder, this will create new Columns based on unique values in each chosen Column:

In [14]:

from safeds.data.tabular.transformation import OneHotEncoder

encoder = OneHotEncoder().fit(titanic, ["sex", "port_embarked"])
encoder.transform(titanic_slice)

from safeds.data.tabular.transformation import OneHotEncoder encoder = OneHotEncoder().fit(titanic, ["sex", "port_embarked"]) encoder.transform(titanic_slice)

Out[14]:

	id	name	sex__male	sex__female	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked__Southampton	port_embarked__Cherbourg	port_embarked__Queenstown	port_embarked__nan	survived
0	0	Abbing, Mr. Anthony	1.0	0.0	42.0	0	0	C.A. 5547	3	7.5500	NaN	1.0	0.0	0.0	0.0	0
1	1	Abbott, Master. Eugene Joseph	1.0	0.0	13.0	0	2	C.A. 2673	3	20.2500	NaN	1.0	0.0	0.0	0.0	0
2	2	Abbott, Mr. Rossmore Edward	1.0	0.0	16.0	1	1	C.A. 2673	3	20.2500	NaN	1.0	0.0	0.0	0.0	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	0.0	1.0	35.0	1	1	C.A. 2673	3	20.2500	NaN	1.0	0.0	0.0	0.0	1
4	4	Abelseth, Miss. Karen Marie	0.0	1.0	16.0	0	0	348125	3	7.6500	NaN	1.0	0.0	0.0	0.0	1
5	5	Abelseth, Mr. Olaus Jorgensen	1.0	0.0	25.0	0	0	348122	3	7.6500	F G63	1.0	0.0	0.0	0.0	1
6	6	Abelson, Mr. Samuel	1.0	0.0	30.0	1	0	P/PP 3381	2	24.0000	NaN	0.0	1.0	0.0	0.0	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	0.0	1.0	28.0	1	0	P/PP 3381	2	24.0000	NaN	0.0	1.0	0.0	0.0	1
8	8	Abrahamsson, Mr. Abraham August Johannes	1.0	0.0	20.0	0	0	SOTON/O2 3101284	3	7.9250	NaN	1.0	0.0	0.0	0.0	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	0.0	1.0	18.0	0	0	2657	3	7.2292	NaN	0.0	1.0	0.0	0.0	1

4. Transform table using RangeScaler, this will scale the values in the chosen Columns to a given range:

In [15]:

from safeds.data.tabular.transformation import RangeScaler

scaler = RangeScaler(0.0, 1.0).fit(titanic, ["age"])
scaler.transform(titanic_slice)

from safeds.data.tabular.transformation import RangeScaler scaler = RangeScaler(0.0, 1.0).fit(titanic, ["age"]) scaler.transform(titanic_slice)

Out[15]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	male	0.524008	0	0	C.A. 5547	3	7.5500	NaN	Southampton	0
1	1	Abbott, Master. Eugene Joseph	male	0.160751	0	2	C.A. 2673	3	20.2500	NaN	Southampton	0
2	2	Abbott, Mr. Rossmore Edward	male	0.198330	1	1	C.A. 2673	3	20.2500	NaN	Southampton	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	0.436325	1	1	C.A. 2673	3	20.2500	NaN	Southampton	1
4	4	Abelseth, Miss. Karen Marie	female	0.198330	0	0	348125	3	7.6500	NaN	Southampton	1
5	5	Abelseth, Mr. Olaus Jorgensen	male	0.311064	0	0	348122	3	7.6500	F G63	Southampton	1
6	6	Abelson, Mr. Samuel	male	0.373695	1	0	P/PP 3381	2	24.0000	NaN	Cherbourg	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	female	0.348643	1	0	P/PP 3381	2	24.0000	NaN	Cherbourg	1
8	8	Abrahamsson, Mr. Abraham August Johannes	male	0.248434	0	0	SOTON/O2 3101284	3	7.9250	NaN	Southampton	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	0.223382	0	0	2657	3	7.2292	NaN	Cherbourg	1

5. Transform table using StandardScaler, this will standardize values of chosen Columns:

In [16]:

from safeds.data.tabular.transformation import StandardScaler

scaler = StandardScaler().fit(titanic, ["age", "travel_class"])
scaler.transform(titanic_slice)

from safeds.data.tabular.transformation import StandardScaler scaler = StandardScaler().fit(titanic, ["age", "travel_class"]) scaler.transform(titanic_slice)

Out[16]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	male	0.841202	0	0	C.A. 5547	0.841916	7.5500	NaN	Southampton	0
1	1	Abbott, Master. Eugene Joseph	male	-1.171763	0	2	C.A. 2673	0.841916	20.2500	NaN	Southampton	0
2	2	Abbott, Mr. Rossmore Edward	male	-0.963526	1	1	C.A. 2673	0.841916	20.2500	NaN	Southampton	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	0.355314	1	1	C.A. 2673	0.841916	20.2500	NaN	Southampton	1
4	4	Abelseth, Miss. Karen Marie	female	-0.963526	0	0	348125	0.841916	7.6500	NaN	Southampton	1
5	5	Abelseth, Mr. Olaus Jorgensen	male	-0.338812	0	0	348122	0.841916	7.6500	F G63	Southampton	1
6	6	Abelson, Mr. Samuel	male	0.008251	1	0	P/PP 3381	-0.352091	24.0000	NaN	Cherbourg	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	female	-0.130574	1	0	P/PP 3381	-0.352091	24.0000	NaN	Cherbourg	1
8	8	Abrahamsson, Mr. Abraham August Johannes	male	-0.685875	0	0	SOTON/O2 3101284	0.841916	7.9250	NaN	Southampton	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	-0.824700	0	0	2657	0.841916	7.2292	NaN	Cherbourg	1

Transform column¶

1. Transform "sex" Column by labeling female values with 1 and male with 0:

In [17]:

titanic_slice.transform_column("sex", lambda row: 1 if row.get_value("sex") == "female" else 0)

titanic_slice.transform_column("sex", lambda row: 1 if row.get_value("sex") == "female" else 0)

Out[17]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	0	42.0	0	0	C.A. 5547	3	7.5500	NaN	Southampton	0
1	1	Abbott, Master. Eugene Joseph	0	13.0	0	2	C.A. 2673	3	20.2500	NaN	Southampton	0
2	2	Abbott, Mr. Rossmore Edward	0	16.0	1	1	C.A. 2673	3	20.2500	NaN	Southampton	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	1	35.0	1	1	C.A. 2673	3	20.2500	NaN	Southampton	1
4	4	Abelseth, Miss. Karen Marie	1	16.0	0	0	348125	3	7.6500	NaN	Southampton	1
5	5	Abelseth, Mr. Olaus Jorgensen	0	25.0	0	0	348122	3	7.6500	F G63	Southampton	1
6	6	Abelson, Mr. Samuel	0	30.0	1	0	P/PP 3381	2	24.0000	NaN	Cherbourg	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	1	28.0	1	0	P/PP 3381	2	24.0000	NaN	Cherbourg	1
8	8	Abrahamsson, Mr. Abraham August Johannes	0	20.0	0	0	SOTON/O2 3101284	3	7.9250	NaN	Southampton	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	1	18.0	0	0	2657	3	7.2292	NaN	Cherbourg	1

2. Transform values of "parents_children" Column into yes or no, depending on whether passenger has children:

In [18]:

titanic_slice.transform_column("parents_children", lambda row: "No" if row.get_value("parents_children") == 0 else "Yes")

titanic_slice.transform_column("parents_children", lambda row: "No" if row.get_value("parents_children") == 0 else "Yes")

Out[18]:

	id	name	sex	age	siblings_spouses	parents_children	ticket	travel_class	fare	cabin	port_embarked	survived
0	0	Abbing, Mr. Anthony	male	42.0	0	No	C.A. 5547	3	7.5500	NaN	Southampton	0
1	1	Abbott, Master. Eugene Joseph	male	13.0	0	Yes	C.A. 2673	3	20.2500	NaN	Southampton	0
2	2	Abbott, Mr. Rossmore Edward	male	16.0	1	Yes	C.A. 2673	3	20.2500	NaN	Southampton	0
3	3	Abbott, Mrs. Stanton (Rosa Hunt)	female	35.0	1	Yes	C.A. 2673	3	20.2500	NaN	Southampton	1
4	4	Abelseth, Miss. Karen Marie	female	16.0	0	No	348125	3	7.6500	NaN	Southampton	1
5	5	Abelseth, Mr. Olaus Jorgensen	male	25.0	0	No	348122	3	7.6500	F G63	Southampton	1
6	6	Abelson, Mr. Samuel	male	30.0	1	No	P/PP 3381	2	24.0000	NaN	Cherbourg	0
7	7	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	No	P/PP 3381	2	24.0000	NaN	Cherbourg	1
8	8	Abrahamsson, Mr. Abraham August Johannes	male	20.0	0	No	SOTON/O2 3101284	3	7.9250	NaN	Southampton	1
9	9	Abrahim, Mrs. Joseph (Sophie Halaut Easu)	female	18.0	0	No	2657	3	7.2292	NaN	Cherbourg	1