Error 'ValueError: Length of values (1) does not match length of index (54)' using pandas pd.to_numeric
Question:
I have the below dataframe:
Financial KPI Year Value
0 Total Revenue TTM 92478000
1 Total Revenue 12/31/2021 89113000
2 Total Revenue 12/31/2020 85528000
3 Total Revenue 12/31/2019 91244000
4 Total Revenue 12/31/2018 91247000
5 Net Income from Continuing & Discontinued Operation TTM 27409000
6 Net Income from Continuing & Discontinued Operation 12/31/2021 31978000
7 Net Income from Continuing & Discontinued Operation 12/31/2020 17894000
8 Net Income from Continuing & Discontinued Operation 12/31/2019 27430000
9 Net Income from Continuing & Discontinued Operation 12/31/2018 28147000
10 Normalized Income TTM 27409000
11 Normalized Income 12/31/2021 31978000
12 Normalized Income 12/31/2020 17894000
13 Normalized Income 12/31/2019 27430000
14 Normalized Income 12/31/2018 28147000
15 Basic EPS TTM
16 Basic EPS 12/31/2021 3.60
17 Basic EPS 12/31/2020 1.88
18 Basic EPS 12/31/2019 2.77
19 Basic EPS 12/31/2018 2.64
20 Net_Profit_Margin TTM 0.2963840048443954
21 Net_Profit_Margin 12/31/2021 0.35884775509746053
22 Net_Profit_Margin 12/31/2020 0.20921803386025628
23 Net_Profit_Margin 12/31/2019 0.3006225066853711
24 Net_Profit_Margin 12/31/2018 0.30847041546571397
25 Price To Earnings TTM 9.125
26 Total Assets TTM
27 Total Assets 12/31/2021 3169495000
28 Total Assets 12/31/2020 2819627000
29 Total Assets 12/31/2019 2434079000
30 Total Assets 12/31/2018 2354507000
31 Total Liabilities Net Minority Interest TTM
32 Total Liabilities Net Minority Interest 12/31/2021 2899429000
33 Total Liabilities Net Minority Interest 12/31/2020 2546703000
34 Total Liabilities Net Minority Interest 12/31/2019 2169269000
35 Total Liabilities Net Minority Interest 12/31/2018 2089182000
36 Total Equity Gross Minority Interest TTM
37 Total Equity Gross Minority Interest 12/31/2021 270066000
38 Total Equity Gross Minority Interest 12/31/2020 272924000
39 Total Equity Gross Minority Interest 12/31/2019 264810000
40 Total Equity Gross Minority Interest 12/31/2018 265325000
41 Total Debt TTM
42 Total Debt 12/31/2021 303870000
43 Total Debt 12/31/2020 282255000
44 Total Debt 12/31/2019 265060000
45 Total Debt 12/31/2018 249529000
46 Current_Ratio (assets/liabilities) 12/31/2021 1.093144546736616
47 Current_Ratio (assets/liabilities) 12/31/2020 1.1071675809860828
48 Current_Ratio (assets/liabilities) 12/31/2019 1.1220733804797838
49 Current_Ratio (assets/liabilities) 12/31/2018 1.126999466776949
50 Debt_to_Assets_Ratio 12/31/2021 0.09587331735812803
51 Debt_to_Assets_Ratio 12/31/2020 0.10010366619414554
52 Debt_to_Assets_Ratio 12/31/2019 0.10889539739671555
53 Debt_to_Assets_Ratio 12/31/2018 0.10597929842637971
I’m trying to convert the values of column ‘Value’ from string to the float type with the following line:
df_global['Value'] = pd.to_numeric(['Value'], errors='coerce')
However, with this line, I’m getting the error:
ValueError: Length of values (1) does not match length of index (54)
Not sure why this is happening. To my understanding the to_numeric function should just convert all the values of that column to float. The index, without the column headers, go from 0 to 53 so why is it complaining? How can I prevent this.
Any help?
Answers:
There is a problem with the way you are passing the column name to the to_numeric() function. You should pass the name of the column as a string, without using square brackets:
df_global['Value'] = pd.to_numeric(df_global['Value'], errors='coerce')
I have the below dataframe:
Financial KPI Year Value
0 Total Revenue TTM 92478000
1 Total Revenue 12/31/2021 89113000
2 Total Revenue 12/31/2020 85528000
3 Total Revenue 12/31/2019 91244000
4 Total Revenue 12/31/2018 91247000
5 Net Income from Continuing & Discontinued Operation TTM 27409000
6 Net Income from Continuing & Discontinued Operation 12/31/2021 31978000
7 Net Income from Continuing & Discontinued Operation 12/31/2020 17894000
8 Net Income from Continuing & Discontinued Operation 12/31/2019 27430000
9 Net Income from Continuing & Discontinued Operation 12/31/2018 28147000
10 Normalized Income TTM 27409000
11 Normalized Income 12/31/2021 31978000
12 Normalized Income 12/31/2020 17894000
13 Normalized Income 12/31/2019 27430000
14 Normalized Income 12/31/2018 28147000
15 Basic EPS TTM
16 Basic EPS 12/31/2021 3.60
17 Basic EPS 12/31/2020 1.88
18 Basic EPS 12/31/2019 2.77
19 Basic EPS 12/31/2018 2.64
20 Net_Profit_Margin TTM 0.2963840048443954
21 Net_Profit_Margin 12/31/2021 0.35884775509746053
22 Net_Profit_Margin 12/31/2020 0.20921803386025628
23 Net_Profit_Margin 12/31/2019 0.3006225066853711
24 Net_Profit_Margin 12/31/2018 0.30847041546571397
25 Price To Earnings TTM 9.125
26 Total Assets TTM
27 Total Assets 12/31/2021 3169495000
28 Total Assets 12/31/2020 2819627000
29 Total Assets 12/31/2019 2434079000
30 Total Assets 12/31/2018 2354507000
31 Total Liabilities Net Minority Interest TTM
32 Total Liabilities Net Minority Interest 12/31/2021 2899429000
33 Total Liabilities Net Minority Interest 12/31/2020 2546703000
34 Total Liabilities Net Minority Interest 12/31/2019 2169269000
35 Total Liabilities Net Minority Interest 12/31/2018 2089182000
36 Total Equity Gross Minority Interest TTM
37 Total Equity Gross Minority Interest 12/31/2021 270066000
38 Total Equity Gross Minority Interest 12/31/2020 272924000
39 Total Equity Gross Minority Interest 12/31/2019 264810000
40 Total Equity Gross Minority Interest 12/31/2018 265325000
41 Total Debt TTM
42 Total Debt 12/31/2021 303870000
43 Total Debt 12/31/2020 282255000
44 Total Debt 12/31/2019 265060000
45 Total Debt 12/31/2018 249529000
46 Current_Ratio (assets/liabilities) 12/31/2021 1.093144546736616
47 Current_Ratio (assets/liabilities) 12/31/2020 1.1071675809860828
48 Current_Ratio (assets/liabilities) 12/31/2019 1.1220733804797838
49 Current_Ratio (assets/liabilities) 12/31/2018 1.126999466776949
50 Debt_to_Assets_Ratio 12/31/2021 0.09587331735812803
51 Debt_to_Assets_Ratio 12/31/2020 0.10010366619414554
52 Debt_to_Assets_Ratio 12/31/2019 0.10889539739671555
53 Debt_to_Assets_Ratio 12/31/2018 0.10597929842637971
I’m trying to convert the values of column ‘Value’ from string to the float type with the following line:
df_global['Value'] = pd.to_numeric(['Value'], errors='coerce')
However, with this line, I’m getting the error:
ValueError: Length of values (1) does not match length of index (54)
Not sure why this is happening. To my understanding the to_numeric function should just convert all the values of that column to float. The index, without the column headers, go from 0 to 53 so why is it complaining? How can I prevent this.
Any help?
There is a problem with the way you are passing the column name to the to_numeric() function. You should pass the name of the column as a string, without using square brackets:
df_global['Value'] = pd.to_numeric(df_global['Value'], errors='coerce')