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 60000Next, 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 70000Now, 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 60000Lastly, 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 70000Conclusion
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.
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 1.616097 0.523411 -0.990174
2 -0.831027 -1.144593 -0.325527
3 -0.634986 0.288986 1.880926
4 1.521251 0.192420 2.066384Now, 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 2.066384Here, 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 1.671674
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 0.407616 -1.014114
9 -1.387213 0.337610 0.791619 0.698169 -0.866450 -0.142285Now, 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 0.791619 0.698169 -0.866450 -0.142285
1 -0.805296 0.243475 0.630819 -1.155929 0.255950 1.045225In 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 -0.846168
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 0.773018Suppose 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 -0.740815In 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 1.201688 -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 2.516187 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 1.516985
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 -0.965542 -0.712311 0.935308 1.296074
17 0.232784 -0.480146 0.640980 -0.634364
18 1.029953 0.405278 0.413166 -0.195263
19 -0.129239 0.479152 -0.040044 0.501988Suppose we want to randomly select 30% of the rows from this DataFrame. We can do that as follows:
random_rows = df.sample(frac=0.3)
print(random_rows)This could output something like:
A B C D
7 -0.078142 -0.679084 2.516187 1.026600
18 1.029953 0.405278 0.413166 -0.195263
14 1.052550 1.369863 0.658469 0.329541
17 0.232784 -0.480146 0.640980 -0.634364
12 -0.070396 -0.306412 -0.787935 1.516985
19 -0.129239 0.479152 -0.040044 0.501988In this example, we set the frac argument to 0.3, which means that we are selecting 30% of the rows from the DataFrame. The output DataFrame has six rows, which is approximately 30% of the total 20 rows in the original DataFrame.