In this short guide, you’ll see how to generate random integers in Pandas DataFrame under:
- Single DataFrame column
- Multiple DataFrame columns
You’ll also see how to convert those integers to different data types, such as floats or strings.
Generate Random Integers under a Single DataFrame Column
Here is a template that you may use to generate random integers under a single DataFrame column:
import numpy as np
import pandas as pd
data = np.random.randint(lowest integer, highest integer, size=number of random integers)
df = pd.DataFrame(data, columns=["column name"])
print(df)
For example, let’s say that you want to generate random integers given the following information:
- The lowest integer is 5 (inclusive)
- The highest integer is 30 (exclusive)
- The size is 10
You may then apply this code in Python:
import numpy as np
import pandas as pd
data = np.random.randint(5, 30, size=10)
df = pd.DataFrame(data, columns=["random_numbers"])
print(df)
When you run the code, you’ll get 10 random integers (as specified by the size of 10):
random_numbers
0 15
1 5
2 24
3 19
4 23
5 24
6 29
7 27
8 25
9 19You may note that the lowest integer (e.g., 5 in the code above) may be included when generating the random integers, but the highest integer (e.g., 30 in the code above) will be excluded.
Generate Random Integers under Multiple DataFrame Columns
Here is a template to generate random integers under multiple DataFrame columns:
import pandas as pd
data = np.random.randint(lowest integer, highest integer, size=(number of random integers per column, number of columns))
df = pd.DataFrame(
data, columns=["column name 1", "column name 2", "column name 3", ...]
)
print(df)
For instance, you can apply the code below in order to create 3 columns with random integers:
import numpy as np
import pandas as pd
data = np.random.randint(5, 30, size=(10, 3))
df = pd.DataFrame(
data, columns=["random_numbers_1", "random_numbers_2", "random_numbers_3"]
)
print(df)
And here is the result:
random_numbers_1 random_numbers_2 random_numbers_3
0 15 5 12
1 27 16 7
2 10 19 17
3 19 13 11
4 5 29 8
5 10 26 14
6 24 11 10
7 20 5 10
8 18 28 25
9 13 22 27Check the Data Type
You can check the data type in Pandas DataFrame by adding print(df.dtypes) at the bottom of the code:
import numpy as np
import pandas as pd
data = np.random.randint(5, 30, size=(10, 3))
df = pd.DataFrame(
data, columns=["random_numbers_1", "random_numbers_2", "random_numbers_3"]
)
print(df)
print(df.dtypes)
As you may observe, the data type for each of the 3 columns is integer:
random_numbers_1 random_numbers_2 random_numbers_3
0 23 10 21
1 27 18 7
2 11 27 14
3 17 29 21
4 27 15 16
5 10 20 23
6 14 16 20
7 21 25 10
8 9 27 6
9 15 26 10
random_numbers_1 int32
random_numbers_2 int32
random_numbers_3 int32Convert the Data Type to Float
You can convert the integers to floats by applying astype(float) as follows:
import numpy as np
import pandas as pd
data = np.random.randint(5, 30, size=(10, 3))
df = pd.DataFrame(
data, columns=["random_numbers_1", "random_numbers_2", "random_numbers_3"]
).astype(float)
print(df)
print(df.dtypes)
You’ll see that the data type for each of the 3 columns is now float:
random_numbers_1 random_numbers_2 random_numbers_3
0 14.0 19.0 26.0
1 8.0 14.0 8.0
2 19.0 22.0 28.0
3 24.0 10.0 29.0
4 11.0 26.0 12.0
5 13.0 12.0 8.0
6 26.0 7.0 11.0
7 22.0 24.0 23.0
8 28.0 8.0 18.0
9 13.0 27.0 26.0
random_numbers_1 float64
random_numbers_2 float64
random_numbers_3 float64Convert the Data Type to String
Alternatively, you may convert the integers to strings using astype(str):
import numpy as np
import pandas as pd
data = np.random.randint(5, 30, size=(10, 3))
df = pd.DataFrame(
data, columns=["random_numbers_1", "random_numbers_2", "random_numbers_3"]
).astype(str)
print(df)
print(df.dtypes)
You’ll now get ‘object’ which represents strings:
random_numbers_1 random_numbers_2 random_numbers_3
0 22 9 26
1 6 28 19
2 21 10 15
3 16 11 21
4 13 16 21
5 9 12 23
6 10 8 27
7 9 14 7
8 29 13 8
9 20 5 25
random_numbers_1 object
random_numbers_2 object
random_numbers_3 objectCheck the numpy manual for further information about numpy.random.randint.