Pandas dataframes are quite versatile when it comes to manipulating 2D tabular data in python. And often it can be quite useful to convert a numpy array to a pandas dataframe for manipulating or transforming data. In this tutorial, we’ll look at how to create a pandas dataframe from a numpy array.

## Using the `pandas.DataFrame()`

function

To create a pandas dataframe from a numpy array, pass the numpy array as an argument to the `pandas.DataFrame()`

function. You can also pass the index and column labels for the dataframe. The following is the syntax:

df = pandas.DataFrame(data=arr, index=None, columns=None)

## Examples

Let’s look at a few examples to better understand the usage of the `pandas.DataFrame()`

function for creating dataframes from numpy arrays.

### 1. 2D numpy array to a pandas dataframe

Let’s create a dataframe by passing a numpy array to the `pandas.DataFrame()`

function and keeping other parameters as default.

import numpy as np import pandas as pd # sample numpy array arr = np.array([[70, 90, 80], [68, 80, 93]]) # convert to pandas dataframe with default parameters df = pd.DataFrame(arr) # print print("Numpy array:\n", arr) print("\nPandas dataframe:\n", df)

Output:

Numpy array: [[70 90 80] [68 80 93]] Pandas dataframe: 0 1 2 0 70 90 80 1 68 80 93

In the above example, the dataframe `df`

is created from the numpy array `arr`

. Note that since we did not pass the index and column labels, the created dataframe used the default RangeIndex for them.

Let’s pass custom index and column labels to the dataframe being created.

import numpy as np import pandas as pd # sample numpy array arr = np.array([[70, 90, 80], [68, 80, 93]]) # convert to pandas dataframe with custom index and column names df = pd.DataFrame(arr, columns=['History', 'Physics', 'Math'], index=['Sam', 'Emma']) # print print("Numpy array:\n", arr) print("\nPandas dataframe:\n", df)

Output:

Numpy array: [[70 90 80] [68 80 93]] Pandas dataframe: History Physics Math Sam 70 90 80 Emma 68 80 93

Here, the index labels and column names are passed to the arguments `index`

and `columns`

respectively. From the labels, we can assume that the dataframe stores the test scores of students `Sam`

and `Emma`

in the subjects `History`

, `Physics`

and `Math`

.

### 2. 1D numpy array to a pandas dataframe

Passing a one-dimensional numpy array to the `pandas.DataFrame()`

function will result in a pandas dataframe with one column.

import numpy as np import pandas as pd # sample numpy array arr = np.array([10, 20, 30, 40]) # convert to pandas dataframe df = pd.DataFrame(arr) # print print("Numpy array:\n", arr) print("\nPandas dataframe:\n", df)

Output:

Numpy array: [10 20 30 40] Pandas dataframe: 0 0 10 1 20 2 30 3 40

Fore more on the `pandas.DataFrame()`

function, refer to its official documentation.

### Additional Note

Pandas dataframes are objects used to store two-dimensional tabular data. If you try to create a pandas dataframe from a numpy array with more than 2 dimensions, you’ll get an error. See the example below.

import numpy as np import pandas as pd # sample numpy array arr = np.random.randint(1,5,(3,3,2)) print("Numpy array:\n", arr) # convert to pandas dataframe df = pd.DataFrame(arr) print("\nPandas dataframe:\n", df)

Output:

Numpy array: [[[4 4] [2 2] [2 2]] [[2 2] [4 4] [1 3]] [[3 4] [4 1] [2 2]]] --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-12-8c5dce07516e> in <module> 6 print("Numpy array:\n", arr) 7 # convert to pandas dataframe ----> 8 df = pd.DataFrame(arr) 9 print("\nPandas dataframe:\n", df) ~\anaconda3\lib\site-packages\pandas\core\internals\construction.py in prep_ndarray(values, copy) 293 values = values.reshape((values.shape[0], 1)) 294 elif values.ndim != 2: --> 295 raise ValueError("Must pass 2-d input") 296 297 return values ValueError: Must pass 2-d input

** * *Some lines in the above error message have been skipped to shorten the output shown.*

With this, we come to the end of this tutorial. The code examples and results presented in this tutorial have been implemented in a Jupyter Notebook with a python (version 3.8.3) kernel having numpy version 1.18.5 and pandas version 1.0.5

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