# How To Convert Numpy Array To Pandas Dataframe?

Numpy arrays are used for array computing. It can be used for performing a number of mathematical operations such as algebraic, trigonometric, and statistical routines.

When you have a NumPy array, you may need to convert it into a pandas dataframe for other data manipulation operations supported by Pandas Dataframe.

You can convert the NumPy array to Pandas Dataframe by using the `pd.DataFrame(array)` method.

In this tutorial, you’ll learn the different methods to convert NumPy Array To Pandas Dataframe.

If You’re in Hurry…

You can use the below code snippet to convert the NumPy array to Pandas Dataframe.

Snippet

``````import numpy as np
import pandas as pd

array = np.random.rand(5, 5)

df = pd.DataFrame(array)

df``````

This is how you can create a pandas dataframe from the NumPy Array.

If You Want to Understand Details, Read on…

In this tutorial, you’ll learn the different methods available to create pandas dataframe from the NumPy Array.

## Creating NumPy Array

First, you’ll create a NumPy array which will be converted to pandas Dataframe.

You can create a NumPy array by using the `np.random.rand()` method. This will create a 5 X 5-dimensional array filled with random values.

Snippet

``````import numpy as np
import pandas as pd

array = np.random.rand(5, 5)

array``````

When you print the array, you’ll see the output of 5 rows and 5 columns with random values.

Output

``````    array([[0.93083461, 0.49167774, 0.43159395, 0.4410153 , 0.80704423],
[0.92919269, 0.58450733, 0.6947164 , 0.6369035 , 0.31362118],
[0.53760608, 0.83053222, 0.3622226 , 0.57997871, 0.83459934],
[0.70689251, 0.32799213, 0.01533952, 0.0212185 , 0.93386042],
[0.13681433, 0.90448399, 0.67102222, 0.45538514, 0.15043999]])``````

Now, you’ll learn how this NumPy array will be converted to Pandas Dataframe.

## Convert Numpy Array to Pandas Dataframe

In this section, you’ll learn how to convert Numpy array to pandas dataframe without using any additional options such as column names or indexes.

You can convert NumPy array to pandas dataframe using the dataframe constructor `pd.DataFrame(array)`.

Use the below snippet to create a pandas dataframe from the NumPy array.

Snippet

``````df = pd.DataFrame(array)

df``````

When you print the dataframe using `df`, you’ll see the array is converted as a dataframe.

DataFrame will look Like

This is how you can create a dataframe using the NumPy array without any additional options.

## Convert NumPy Array to Pandas Dataframe with Column Names

In this section, you’ll learn how to convert NumPy array to pandas dataframe with column names.

Typically, NumPy arrays don’t have column names. Hence, while converting the NumPy arrays to Pandas dataframe, there will not be any column names assigned to the dataframe.

You can convert NumPy Array to pandas dataframe with column names using the attribute `columns` and passing the column values as a list.

Use the below snippet to convert the NumPy array to pandas dataframe with column names.

The list of column values must be in the same dimension as the array columns. If you’ve `5` columns in the array, then you need to pass 5 values in the list.

Snippet

``````df = pd.DataFrame(array, columns = ['Col_one', 'Col_two', 'Col_Three', 'Col_Four', 'Col_Five'])

df``````

When you print the dataframe using `df`, you’ll see that columns in the dataframe are named accordingly.

DataFrame will look Like

This is how you can create a pandas dataframe using the NumPy array with column values.

## Convert Numpy Array to Pandas Dataframe with Index

In this section, you’ll learn how to convert NumPy array to pandas dataframe with index.

Typically, NumPy arrays don’t have row indexes. Hence, while converting the NumPy arrays to Pandas dataframe, there will not be any indexes assigned to the dataframe.

You can convert NumPy Array to pandas dataframe with index using the attribute `index` and passing the index values as a list.

Use the below snippet to convert NumPy array to pandas dataframe with index.

The list of index values must be in the same dimension as the array rows. If you’ve `5` rows in the array, then you need to pass 5 values in the index list.

Snippet

``````df = pd.DataFrame(array, columns = ['Col_one', 'Col_two', 'Col_Three', 'Col_Four', 'Col_Five'],  index = ['Row_1', 'Row_2','Row_3','Row_4','Row_5'])

df``````

When you print the dataframe using `df`, you’ll see that rows in the dataframe are named using the passed indexes accordingly.

DataFrame will look Like

This is how you can create a pandas dataframe with a NumPy array with index values.

## Convert Object Type NumPy array to Dataframe

Until now, you’ve learned how to convert NumPy array which has the same type of data to a pandas dataframe.

In this section, you’ll learn how to convert object type NumPy array which has different types of data in each column to a pandas dataframe.

First, create a NumPy.ndarray with String value in one column and int value in one column.

For example,

• First column has country names which are of `String` type
• Second column has a country codes which are of `Int` type.

Snippet

``````import numpy as np

arr = np.array([['India',1],['Germany',2],['US',3]], dtype=object)

print(arr)
print(type(arr))
print(arr.dtype)``````

Output

``````    [['India' 1]
['Germany' 2]
['US' 3]]
<class 'numpy.ndarray'>
object``````

Now, you’ll convert this ndarray into a dataframe object.

You can use the `DataFrame()` constructor available in the pandas library to convert Numpy ndarray to a dataframe.

You can also pass the name for columns using the `columns[]` attribute as shown below.

Snippet

``````df = pd.DataFrame(arr, columns = ['Country', 'Code'])

df``````

When you print the dataframe, you’ll see the dataframe with two columns named.

DataFrame will look Like

You can check the type of the dataframe columns using the below snippet.

Snippet

``df.dtypes``

You can see both the columns are created as objects rather than creating the `code` column as a number. If you want to convert code column to number, read Change column type in Pandas.

Output

``````Country       object
Code          object
dtype: object``````

## Concatenate NumPy Array to Pandas Dataframe

In the previous sections, you’ve learned how to create a Pandas dataframe from the NumPy array.

In this section, you’ll learn how to concatenate the NumPy array to the existing pandas dataframe. This is also known as adding a NumPy array to pandas dataframe.

First, create a NumPy array with two columns namely Country and Code. Then create a dataframe called `df` using `pd.DataFrame()` method.

Next, create a second NumPy array with one column called countries. After creating a second NumPy array, you cannot directly concatenate with the existing dataframe. You need to create a separate dataframe for the new NumPy Array and then concatenate two data frames.

You can concatenate the second dataframe to the first dataframe using the assignment operator as shown below.

Snippet

``````import numpy as np

arr = np.array([['India',1],['Germany',2],['US',3]], dtype=object)

df = pd.DataFrame(arr, columns = ['Country', 'Code'])

arr1 = np.array([['India'],['Germany'],['US']], dtype=object)

df2 = pd.DataFrame(arr1, columns = ['Country'])

df['New_Column'] = df2['Country']

df``````

When you print the dataframe `df`, you’ll see the second NumPy array appended to the first dataframe.

DataFrame will look Like

This is how you can Add Numpy Array to Pandas Dataframe using the dataframe append method.

## Conclusion

To summarize, you’ve learned how to convert a NumPy array to a pandas dataframe. This is also known as creating a pandas dataframe from a NumPy array.

Additionally, you’ve learned how to convert pandas dataframe with column names and indexes. Also, you’ve learned how to convert NumPy arrays with different column types to a dataframe and convert the column types of the column in the dataframe.

If you have any questions, comment below.