Python Pandas Valueerror Arrays Must Be All Same Length

Python Pandas "All arrays must be of the same length" Problem

You can try this.

a = {"Ana Linkler":linkler, "İç Linkler":ic_linkler, "Sorular":sorular}
df = pd.DataFrame.from_dict(a, orient='index')

ValueError: arrays must all be same length in python using pandas DataFrame

I'd just construct a Series for each list and then concat them all:

In [38]:
l1 = list('abc')
l2 = [1,2,3,4]
s1 = pd.Series(l1, name='list1')
s2 = pd.Series(l2, name='list2')
df = pd.concat([s1,s2], axis=1)
df

Out[38]: 
  list1  list2
0     a      1
1     b      2
2     c      3
3   NaN      4

As you can pass a name arg for the Series ctor it will name each column in the df, plus it will place NaN where the column lengths don't match

resample refers to when you have a DatetimeIndex for which you want to rebase or adjust the length based on some time period which is not what you want here. You want to reindex which I think is unnecessary and messy:

In [40]:
l1 = list('abc')
l2 = [1,2,3,4]
s1 = pd.Series(l1)
s2 = pd.Series(l2)
df = pd.DataFrame({'list1':s1.reindex(s2.index), 'list2':s2})
df

Out[40]: 
  list1  list2
0     a      1
1     b      2
2     c      3
3   NaN      4

Here you'd need to know the longest length and then reindex all Series using that index, if you just concat it will automatically adjust the lengths and fill missing elements with NaN

ValueError: arrays must all be same length while converting dictionary to data frame

Use DataFrame.from_dict with orient='index':

df = pd.DataFrame.from_dict(a, orient='index')
print (df)
         0    1          2     3     4     5
var1  LPES    A       None  None  None  None
var2     F    D       None  None  None  None
var3     R    T  EDUCATION     A     B      
var4    HI   HI             None  None  None
var5    PP  CCM         PP        None  None
var6     C            None  None  None  None

If need 2 columns DataFrame use flatten list comprehension for list of tuples:

df = pd.DataFrame([(k, x) for k, v in a.items() for x in v], columns=['a','b'])
print (df)
       a          b
0   var1       LPES
1   var1          A
2   var2          F
3   var2          D
4   var3          R
5   var3          T
6   var3  EDUCATION
7   var3          A
8   var3          B
9   var3           
10  var4         HI
11  var4         HI
12  var4           
13  var5         PP
14  var5        CCM
15  var5         PP
16  var5           
17  var6          C
18  var6           

Pandas and JSON ValueError: arrays must all be same length

You have different lengths if rows so your original code will fail.

Try this:

import json
from pandas.io.json import json_normalize

with open('Lyrics_SteelyDan.json') as json_data:
    data = json.load(json_data)

df = pd.DataFrame(data['songs'])
df['lyrics']

Read also this: https://hackersandslackers.com/json-into-pandas-dataframes/

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