2.3. Cleaning the DataFrame#

2.3.1. How to Drop a Column in Pandas DataFrame#

import pandas as pd
df = pd.read_csv("../data/titanic.csv")
df
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
... ... ... ... ... ... ... ... ... ... ... ... ...
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
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

891 rows × 12 columns

Imagine that we have a large DataFrame, but we are not interested in a couple columns. This is especially import when your DataFrame has 10s or 100s of columns. In these instances, you need to examine the DataFrame without the useless data. Imagine that we wanted to study the Titanic data but knew that Parch and Ticket were categories that we did not need. We can use df.drop() to pass an argument to remove those specific columns.

df.drop(columns=["Parch", "Ticket"])
PassengerId Survived Pclass Name Sex Age SibSp Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22.0 1 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26.0 0 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35.0 0 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ...
886 887 0 2 Montvila, Rev. Juozas male 27.0 0 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 30.0000 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 23.4500 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 7.7500 NaN Q

891 rows × 10 columns

Likewise, we can do the opposite. Rather than dropping certain columns, we can keep certain columns with the example code below.

df[["Survived", "Name"]]
Survived Name
0 0 Braund, Mr. Owen Harris
1 1 Cumings, Mrs. John Bradley (Florence Briggs Th...
2 1 Heikkinen, Miss. Laina
3 1 Futrelle, Mrs. Jacques Heath (Lily May Peel)
4 0 Allen, Mr. William Henry
... ... ...
886 0 Montvila, Rev. Juozas
887 1 Graham, Miss. Margaret Edith
888 0 Johnston, Miss. Catherine Helen "Carrie"
889 1 Behr, Mr. Karl Howell
890 0 Dooley, Mr. Patrick

891 rows × 2 columns

Note the use of double brackets here, e.g. [[]].

2.3.2. How to Remove Rows that have NaN in any Column#

One of the biggest problems in datasets is the absence of data. If you are training a machine learning model or just performing quantitative analysis, rows that have missing values, or NaN, can radically alter your results. It is often good practice to ignore that data or alter it in some way. Let’s presume that we want to simply remove it from our dataset. To do that, we can use df.dropna() which will remove all rows that have any instance of NaN in any column.

df.dropna()
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
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
... ... ... ... ... ... ... ... ... ... ... ... ...
871 872 1 1 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 1 11751 52.5542 D35 S
872 873 0 1 Carlsson, Mr. Frans Olof male 33.0 0 0 695 5.0000 B51 B53 B55 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
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.0000 C148 C

183 rows × 12 columns

2.3.3. How to Remove Rows that have NaN in a Specific Column#

In some instances, though, we don’t want to remove an entire row just because of NaN in one column. Maybe that column is not as important for quantitative analysis and we are not planning to include it in our analysis, but we still want to see it. A good example of this is the column Cabin which is a string or Age which is a float (we’ll get to that in a moment). Let’s say we want to remove all rows that have NaN in the Age column. We can use the command below.

df2 = df[df["Age"].notna()]

df2
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
... ... ... ... ... ... ... ... ... ... ... ... ...
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39.0 0 5 382652 29.1250 NaN Q
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
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.7500 NaN Q

714 rows × 12 columns

As we can see, the size of our DataFrame dropped from 891 rows to 714.

2.3.4. How to Convert DataFrame Data Types (from Float to Int)#

In other instances, it may be important not to simply remove a column, but alter it into a different type of data. In this dataset, Age is a float. This is to account for infants who were below the age of 1 on the Titanic. Let’s presume that we want to convert all these floats to integers. To do that we can use the .astype() method on a specific row.

df2.Age = df2.Age.astype(int)

/tmp/ipykernel_708450/3009824114.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  df2.Age = df2.Age.astype(int)

df2
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 1 0 3 Braund, Mr. Owen Harris male 22 1 0 A/5 21171 7.2500 NaN S
1 2 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38 1 0 PC 17599 71.2833 C85 C
2 3 1 3 Heikkinen, Miss. Laina female 26 0 0 STON/O2. 3101282 7.9250 NaN S
3 4 1 1 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35 1 0 113803 53.1000 C123 S
4 5 0 3 Allen, Mr. William Henry male 35 0 0 373450 8.0500 NaN S
... ... ... ... ... ... ... ... ... ... ... ... ...
885 886 0 3 Rice, Mrs. William (Margaret Norton) female 39 0 5 382652 29.1250 NaN Q
886 887 0 2 Montvila, Rev. Juozas male 27 0 0 211536 13.0000 NaN S
887 888 1 1 Graham, Miss. Margaret Edith female 19 0 0 112053 30.0000 B42 S
889 890 1 1 Behr, Mr. Karl Howell male 26 0 0 111369 30.0000 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32 0 0 370376 7.7500 NaN Q

714 rows × 12 columns

Now our Age column is no longer a float, rather an integer.

2.3.5. Conclusion#

Sorting, cleaning, and organizing data in Pandas can require practice. Even after years of using Pandas, you will still find yourself looking things up on Stackoverflow or other resources online. The power of Pandas comes at the cost of making it difficult to master quickly. With regular practice, however, Pandas does get easier to use over time. Once you have a command of Pandas, you can do quick data cleaning and analysis all in Python. In the next chapter, we will look at how leverage Pandas to do more advanced searching methods.