Python: create a pandas data frame from a list

Question

I am using the following code to create a data frame from a list:

test_list = ['a','b','c','d']
df_test = pd.DataFrame.from_records(test_list, columns=['my_letters'])
df_test

The above code works fine. Then I tried the same approach for another list:

import pandas as pd
q_list = ['112354401', '116115526', '114909312', '122425491', '131957025', '111373473']
df1 = pd.DataFrame.from_records(q_list, columns=['q_data'])
df1

But it gave me the following errors this time:

---------------------------------------------------------------------------
AssertionError                            Traceback (most recent call last)
<ipython-input-24-99e7b8e32a52> in <module>()
      1 import pandas as pd
      2 q_list = ['112354401', '116115526', '114909312', '122425491', '131957025', '111373473']
----> 3 df1 = pd.DataFrame.from_records(q_list, columns=['q_data'])
      4 df1

/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in from_records(cls, data, index, exclude, columns, coerce_float, nrows)
   1021         else:
   1022             arrays, arr_columns = _to_arrays(data, columns,
-> 1023                                              coerce_float=coerce_float)
   1024 
   1025             arr_columns = _ensure_index(arr_columns)

/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in _to_arrays(data, columns, coerce_float, dtype)
   5550         data = lmap(tuple, data)
   5551         return _list_to_arrays(data, columns, coerce_float=coerce_float,
-> 5552                                dtype=dtype)
   5553 
   5554 

/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in _list_to_arrays(data, columns, coerce_float, dtype)
   5607         content = list(lib.to_object_array(data).T)
   5608     return _convert_object_array(content, columns, dtype=dtype,
-> 5609                                  coerce_float=coerce_float)
   5610 
   5611 

/usr/local/lib/python3.4/dist-packages/pandas/core/frame.py in _convert_object_array(content, columns, coerce_float, dtype)
   5666             # caller's responsibility to check for this...
   5667             raise AssertionError('%d columns passed, passed data had %s '
-> 5668                                  'columns' % (len(columns), len(content)))
   5669 
   5670     # provide soft conversion of object dtypes

AssertionError: 1 columns passed, passed data had 9 columns

Why would the same approach work for one list but not another? Any idea what might be wrong here? Thanks a lot!

Arya McCarthy · Accepted Answer · 2020-05-29 15:29:11Z

155

DataFrame.from_records treats string as a character list. so it needs as many columns as length of string.

You could simply use the DataFrame constructor.

In [3]: pd.DataFrame(q_list, columns=['q_data'])
Out[3]:
      q_data
0  112354401
1  116115526
2  114909312
3  122425491
4  131957025
5  111373473

edited May 29, 2020 at 15:29

Arya McCarthy

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answered Apr 3, 2017 at 1:56

gzc

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Comments

Farid Khafizov · Accepted Answer · 2020-01-23 03:27:46Z

50

In[20]: test_list = [['a','b','c'], ['AA','BB','CC']]

In[21]: pd.DataFrame(test_list, columns=['col_A', 'col_B', 'col_C'])
Out[21]: 
  col_A col_B col_C
0     a     b     c
1    AA    BB    CC

In[22]: pd.DataFrame(test_list, index=['col_low', 'col_up']).T
Out[22]: 
  col_low col_up
0       a     AA
1       b     BB
2       c     CC

answered Jan 23, 2020 at 3:27

Farid Khafizov

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Comments

Vivek Ramanathan · Accepted Answer · 2020-11-24 16:32:55Z

36

If you want to create a DataFrame from multiple lists you can simply zip the lists. This returns a 'zip' object. So you convert back to a list.

mydf = pd.DataFrame(list(zip(lstA, lstB)), columns = ['My List A', 'My List B'])

answered Nov 24, 2020 at 16:32

Vivek Ramanathan

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1 Comment

YugoAmaryl Over a year ago

I guess this approach is the most commonly used

vpatial · Accepted Answer · 2022-04-25 15:49:37Z

1

You could also take the help of numpy.

import numpy as np
df1 = pd.DataFrame(np.array(q_list),columns=['q_data'])

answered Apr 25, 2022 at 15:49

vpatial

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Comments

halfer · Accepted Answer · 2024-01-27 00:17:14Z

You can follow this code to convert a list to pandas dataframe. I'm here using 2 variables:

Our input list is:

[
  [1, 15],
  [2, 11],
  [3, 11],
  [4, 20]
]

Code:

from typing import List
import pandas as pd

def createDataframe(student_data: List[List[int]]) -> pd.DataFrame: 
    df = pd.DataFrame(student_data, columns=['student_id', 'age'])
    
    return df

df = createDataframe([[1,15],[2,11],[3,11],[4,20]])
# print("createDataframe: ", df)
# print("type of output: ", type(df))

Output:

+------------+-----+
| student_id | age |
+------------+-----+
| 1          | 15  |
| 2          | 11  |
| 3          | 11  |
| 4          | 20  |
+------------+-----+

faeze saghafi · Accepted Answer · 2022-11-24 12:58:46Z

-1

just using concat method

test_list = ['a','b','c','d']
pd.concat(test_list )

answered Nov 24, 2022 at 12:58

faeze saghafi

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1 Comment

Anthony O Over a year ago

You cannot concatenate a built-in List object to a Pandas DataFrame

Glottis4 · Accepted Answer · 2023-12-10 01:43:11Z

-1

You can create a Pandas Data Frame using multiple lists. All the lists must be the same length.

list1 = [0,1,2]

list2 = ['a','b','c']

df = pd.DataFrame({'list1_name':list1, 'list2_name':list2},columns=['list1_name', 'list2_name'])

edited Dec 10, 2023 at 1:43

answered Dec 5, 2023 at 21:28

Glottis4

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