## Add A 1-D Numpy Array to DataFrame as a Row

Is there a function which allows you to efficiently append a NumPy array directly to a DataFrame?

Variables:

df = pd.DataFrame(columns=['col1', 'col2', 'col3']) Out[1]: +------+------+------+ | Col1 | Col2 | Col3 | +------+------+------+ | | | | +------+------+------+ arr = np.empty(3) # array is populated with values. Random numbers are chosen in this example, # but in my program, the numbers are not arbitrary. arr[0] = 756 arr[1] = 123 arr[2] = 452 Out[2]: array([756, 123, 452])

How do I directly append `arr`

to the end of `df`

to get this?

+------+------+------+ | Col1 | Col2 | Col3 | +------+------+------+ | 756 | 123 | 452 | +------+------+------+

I've tried using `df.append(arr)`

but it doesn't accept NumPy arrays. I could convert the NumPy array into a DataFrame then append it, but I think that would be very inefficient, especially over millions of iterations. Is there a more efficient way to do it?

@rafaelc comment can work only if your Pandas DataFrame is indexed from 0 to len(df)-1, so it is not a general workaround and it can easily produce a silent bug in your code.

If you are sure that your Numpy array has the same columns of your Pandas DataFrame you could try using the *append* function with a dict comprehension as follows:

data_to_append = {} for i in range(len(df.columns)): data_to_append[df.columns[i]] = arr[i] df = df.append(data_to_append, ignore_index = True)

You need to reassign the DataFrame because `append`

function does not support in-place modification.

I hope it helps.

**pandas.DataFrame.append,** Append rows of other to the end of this frame, returning a new object. Columns not in this frame are added as new columns. Parameters: other : DataFrame or Here is other example: import numpy as np import pandas as pd """ This just creates a list of touples, and each element of the touple is an array""" a = [ (np.random.randint(1,10,10), np.array([0,1,2,3,4,5,6,7,8,9])) for i in range(0,10) ] """ Panda DataFrame will allocate each of the arrays , contained as a touple element , as column""" df = pd.DataFrame(data =a,columns=['random_num

@BalrogOfMoira is that really faster than simply creating the dataframe to append?

df.append(pd.DataFrame(arr.reshape(1,-1), columns=list(df)), ignore_index=True)

Otherwise @Wonton you could simply concatenate arrays then write to a data frame, which could the be appended to the original data frame.

**pandas.DataFrame,** Two-dimensional size-mutable, potentially heterogeneous tabular data structure as_matrix([columns]), Convert the frame to its Numpy-array representation. Converting a 2D numpy array to dataframe rows. Ask Question Asked 2 years, 10 months ago. Active 2 years, 10 months ago. Add one row to pandas DataFrame. 1126.

This will work:

df.append(pd.DataFrame(arr).T)

**Intro to data structures,** In [1]: import numpy as np In [2]: import pandas as pd Series is a one-dimensional labeled array capable of holding any data type (integers, strings, floating point numbers, Python objects, etc.). The basic method to create a Series is to call: When the data is a dict, and columns is not specified, the DataFrame columns So, the simple solution to the original question, from seven years ago, is to use vstack () to add a new row after converting the row to a numpy array. But a more realistic solution should consider vstack's poor performance under those circumstances.

**Essential basic functionality,** to_numpy() gives some control over the dtype of the resulting numpy.ndarray . For heterogeneous data (e.g. some of the DataFrame's columns are not all the d NaN 0.558688 -1.226343 In [45]: df.add(df2, fill_value=0) Out[45]: one two Pandas DataFrame is two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). This data structure can be converted to NumPy ndarray with the help of Dataframe.to_numpy() method. Syntax: Dataframe.to_numpy(dtype = None, copy = False) Parameters: dtype: Data type which we are passing like

**Add numpy array as column to Pandas data frame,** Using the below code: import numpy as np. import pandas as pd. import scipy.sparse as sparse. df = pd.DataFrame(np.arange(1,10).reshape(3 Pandas DataFrame – Add or Insert Row. To append or add a row to DataFrame, create the new row as Series and use DataFrame.append() method. In this tutorial, we shall learn how to append a row to an existing DataFrame, with the help of illustrative example programs. Syntax – append() Following is the syntax of DataFrame.appen() function.

**How to create Pandas DataFrame from a Numpy array in Python,** A Pandas DataFrame is a two-dimensional data structure that can be created using the data from a NumPy array . with data set to a NumPy array, index set to a list of row names, and column set to a list column1 column2 row1 1 2 row2 3 4 Create a list of dictionaries in which each dictionary corresponds to an input data row. Create a data frame from this list. I had a similar task for which appending to a data frame row by row took 30 min, and creating a data frame from a list of dictionaries completed within seconds.