Adventures in Machine Learning

Randomly Selecting Rows in Pandas DataFrame: Tips and Techniques

Pandas DataFrame is a powerful tool used to store and manipulate data in Python. One common task is to randomly select rows from a DataFrame.

This article will explore four different scenarios for selecting rows at random, including randomly selecting a single row, a specified number of rows, the same row multiple times, and a specified fraction of the total number of rows. Scenario 1: Randomly Selecting a Single Row

Sometimes you’ll want to randomly select a single row from a DataFrame.

This can be done easily using the Pandas `sample()` method. For example, if you have a DataFrame called `df` and want to randomly select one row from it, you can use the following code:

“`

random_row = df.sample(n=1)

“`

This code will return a new DataFrame with a single randomly selected row from the original DataFrame.

Scenario 2: Randomly Selecting a Specified Number of Rows

In some cases, you may want to randomly select a specified number of rows from a DataFrame. This can be achieved by passing the desired number of rows to the `n` argument of the `sample()` method.

For example, if you want to randomly select three rows from a DataFrame called `df`, you can do the following:

“`

random_rows = df.sample(n=3)

“`

This code will return a new DataFrame with three randomly selected rows from the original DataFrame. Scenario 3: Allowing a Random Selection of the Same Row More Than Once

By default, the `sample()` method will not select the same row more than once.

However, you can allow for the same row to be selected multiple times by passing `replace=True` to the `sample()` method. For example, if you want to randomly select five rows from a DataFrame called `df` and allow the same row to be selected more than once, you can do the following:

“`

random_rows = df.sample(n=5, replace=True)

“`

This code will return a new DataFrame with five randomly selected rows, allowing for the same row to be selected more than once.

Scenario 4: Randomly Selecting a Specified Fraction of the Total Number of Rows

In some cases, you may want to randomly select a specified fraction of the total number of rows in a DataFrame. This can be done by passing a fraction value between 0 and 1 to the `frac` argument of the `sample()` method.

For example, if you want to randomly select 20% of the rows from a DataFrame called `df`, you can do the following:

“`

random_rows = df.sample(frac=0.2)

“`

This code will return a new DataFrame with 20% of the rows randomly selected from the original DataFrame.

Example

Let’s consider an example to see how these scenarios work in practice. Suppose we have a DataFrame called `data` with ten rows of data:

“`

import pandas as pd

data = pd.DataFrame({

‘Name’: [‘Alice’, ‘Bob’, ‘Charlie’, ‘David’, ‘Emily’, ‘Frank’, ‘Grace’, ‘Henry’, ‘Isabel’, ‘Jack’],

‘Age’: [23, 34, 45, 29, 26, 37, 25, 31, 39, 28],

‘Salary’: [50000, 70000, 60000, 80000, 65000, 75000, 55000, 90000, 85000, 60000]

})

“`

We can now apply the different scenarios to select rows at random. For example, let’s randomly select a single row:

“`

random_row = data.sample(n=1)

print(random_row)

“`

This could output something like:

“`

Name Age Salary

2 Charlie 45 60000

“`

Next, let’s randomly select three rows:

“`

random_rows = data.sample(n=3)

print(random_rows)

“`

This could output something like:

“`

Name Age Salary

5 Frank 37 75000

3 David 29 80000

1 Bob 34 70000

“`

Now, let’s randomly select five rows, allowing the same row to be selected multiple times:

“`

random_rows = data.sample(n=5, replace=True)

print(random_rows)

“`

This could output something like:

“`

Name Age Salary

9 Jack 28 60000

5 Frank 37 75000

5 Frank 37 75000

3 David 29 80000

9 Jack 28 60000

“`

Lastly, let’s randomly select 20% of the rows:

“`

random_rows = data.sample(frac=0.2)

print(random_rows)

“`

This could output something like:

“`

Name Age Salary

2 Charlie 45 60000

0 Alice 23 50000

1 Bob 34 70000

“`

Conclusion

In conclusion, randomly selecting rows from a Pandas DataFrame is a simple task that can be achieved using the `sample()` method. By applying the different scenarios covered in this article, including randomly selecting a single row, a specified number of rows, the same row multiple times, and a specified fraction of the total number of rows, you can extract random subsets of data for further analysis or modeling.

In data analysis, it is often necessary to randomly select one or more rows from a Pandas DataFrame. This can be useful for various tasks, such as creating a random sample of data for analysis, testing new machine learning models, or evaluating the accuracy of existing models.

In this article, we will explore two scenarios – randomly selecting a single row and randomly selecting a specified number of rows – in detail. Scenario 1: Randomly Selecting a Single Row

To randomly select a single row from a Pandas DataFrame, we can make use of the `sample()` method.

By default, this method returns a single random row from the DataFrame. For example, consider the following code that creates a small DataFrame with three columns and five rows:

“`

import pandas as pd

import numpy as np

df = pd.DataFrame(np.random.randn(5, 3), columns=list(‘ABC’))

print(df)

“`

This would output something like:

“`

A B C

0 0.339272 -0.086331 0.784666

1 1.6

16097 0.523411 -0.990174

2 -0.831027 -1.144593 -0.325527

3 -0.634986 0.288986 1.880926

4 1.521251 0.192420 2.066384

“`

Now, let’s randomly select one row from this DataFrame:

“`

random_row = df.sample()

print(random_row)

“`

This could output something like:

“`

A B C

4 1.521251 0.19242 2.066384

“`

Here, we are using the `sample()` method without any arguments, which means that it will return one random row from the DataFrame. We assign this output to a variable called `random_row` and then print it out to see the result.

Scenario 2: Randomly Selecting a Specified Number of Rows

In some cases, we may need to randomly select more than one row from a DataFrame. We can do this by specifying the number of rows we want to select using the `n` argument of the `sample()` method.

For example, consider the following code that creates a larger DataFrame with six columns and ten rows:

“`

import pandas as pd

import numpy as np

df = pd.DataFrame(np.random.randn(10, 6), columns=list(‘ABCDEF’))

print(df)

“`

This would output something like:

“`

A B C D E F

0 -0.358580 -1.139404 -2.305676 1.186586 -0.864569 -0.258499

1 -0.805296 0.243475 0.630819 -1.155929 0.255950 1.045225

2 -2.211502 0.501180 -1.146020 1.159633 0.461144 1.67

1674

3 -0.345878 0.235535 0.184380 0.510395 0.171964 0.905787

4 0.394561 -0.984404 -0.732232 1.791055 -1.059840 0.550144

5 -0.664236 0.129366 1.039731 1.017206 -1. 169273 -0.847978

6 0.254744 0.621966 -0.398254 -0.466880 -0.594696 -1.742941

7 -0.

166153 0.303472 0.708957 2.188032 0.453950 0.177100

8 0.178306 -0.015237 -1.076698 1.255315 0.4076

16 -1.014114

9 -1.387213 0.337610 0.79

1619 0.698

169 -0.866450 -0.142285

“`

Now, let’s randomly select three rows from this DataFrame:

“`

random_rows = df.sample(n=3)

print(random_rows)

“`

This could output something like:

“`

A B C D E F

0 -0.358580 -1.139404 -2.305676 1.186586 -0.864569 -0.258499

9 -1.387213 0.337610 0.79

1619 0.698

169 -0.866450 -0.142285

1 -0.805296 0.243475 0.630819 -1.155929 0.255950 1.045225

“`

In this case, we are using the `sample()` method with the `n` argument set to 3. This means that the method will return three random rows from the DataFrame.

We assign this output to a variable called `random_rows` and then print it out to see the result. Note that the resulting DataFrame has only three rows as expected.

Closing Thoughts

In this article, we have covered two scenarios for randomly selecting rows from a Pandas DataFrame. We learned how to randomly select a single row using the `sample()` method and how to randomly select a specified number of rows by setting the `n` argument of the `sample()` method.

These techniques can be helpful for various types of data analysis and modeling tasks. With the help of Pandas DataFrame, data analysts can manipulate and select data with ease.

In a Pandas DataFrame, we often need to randomly select rows for various data analysis tasks. However, sometimes, we may need to randomly select a single row or multiple rows repeatedly.

In such cases, we can use the `replace` argument with the `sample()` method to allow the same row to be selected more than once. Additionally, we may want to randomly select a fraction of rows from a DataFrame instead of a specified number.

This article will explore these two scenarios in detail. Scenario 3: Allowing a Random Selection of the Same Row More Than Once

By default, the `sample()` method will not select the same row more than once, which means that it will return distinct rows.

But sometimes, we may need to allow the same row to be selected more than once. In such cases, we can set the `replace` argument of the `sample()` method to `True`.

Here’s an example:

“`

import pandas as pd

import numpy as np

df = pd.DataFrame(np.random.randn(10, 3), columns=[“A”, “B”, “C”])

print(df)

“`

This would output something like:

“`

A B C

0 1.115928 0.883291 1.264322

1 -0.275240 -1.361444 -0.846

168

2 -0.811741 1.325858 -0.372514

3 0.736090 0.646690 1.413807

4 -0.338909 0.905970 -0.740815

5 0.070618 0.379968 -0.561391

6 1.113491 0.544064 2.315895

7 1.456761 0. 169878 -0.926120

8 -1.836084 -0.370047 0.335404

9 -1.149049 0.

163240 0.773018

“`

Suppose we want to randomly select five rows from this DataFrame, allowing the same row to be selected multiple times. We can do that as follows:

“`

random_rows = df.sample(n=5, replace=True)

print(random_rows)

“`

This could output something like:

“`

A B C

8 -1.836084 -0.370047 0.335404

8 -1.836084 -0.370047 0.335404

8 -1.836084 -0.370047 0.335404

6 1.113491 0.544064 2.315895

4 -0.338909 0.905970 -0.740815

“`

In this example, we set the `n` argument to 5, and the `replace` argument to `True`, so that the same rows can be selected multiple times. The output DataFrame has five randomly selected rows, and some rows appear more than once, as we allowed duplicates in the selection.

Scenario 4: Randomly Selecting a Specified Fraction of the Total Number of Rows

In some cases, we may need to randomly select a subset of rows from a DataFrame based on the fraction of the total number of rows we want to select. We can use the `frac` argument of the `sample()` method to specify the fraction of rows we want to select.

Here’s an example:

“`

import pandas as pd

import numpy as np

df = pd.DataFrame(np.random.randn(20, 4), columns=[“A”, “B”, “C”, “D”])

print(df)

“`

This would output something like:

“`

A B C D

0 -0.017072 -1.241110 -0.982369 -1. 164214

1 1.122554 -0.526315 -0.142467 -0.294174

2 -1.367844 1.20

1688 -1.031357 -0.035863

3 -0.622627 0.307924 0.875574 0.839683

4 -1.125712 1.462146 -0.091262 -0.351275

5 0.251265 -0.839477 -0.464835 2.048619

6 0.150735 -0.784646 0.451235 1.302900

7 -0.078142 -0.679084 2.5

16187 1.026600

8 -2.282276 1.281003 -0.381260 -0.782715

9 0.453664 0.317749 1.931088 0.025128

10 -0.061358 0.524406 -2.309487 -0.289667

11 1.154774 0.421366 -0.047018 2.666129

12 -0.070396 -0.306412 -0.787935 1.5

16985

13 -0.027075 1.601350 -0.368911 0.931278

14 1.052550 1.369863 0.658469 0.329541

15 -0.929230 -0.222989 -0.364938 -1.071173

16

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