Issue pivoting a DataFrame - python - ValueError: Length of passed values is X index implies YSet value for particular cell in pandas DataFrame using indexIndex contains multiples value dataframe pivotsConstructing pandas DataFrame from values in variables gives “ValueError: If using all scalar values, you must pass an index”Stack and Pivot Dataframe in PythonPandas: ValueError when pivoting dataframe with MultiIndexHow to pivot a set of multiple columns into a set of flagged values through DataFrame while not wanting to pivot all columnsreorder multi index dataframe with pivotPython pivot DataFrame without index columnsPython pivot dataframe valuesPivot dataframe in Python
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Issue pivoting a DataFrame - python - ValueError: Length of passed values is X index implies Y
Set value for particular cell in pandas DataFrame using indexIndex contains multiples value dataframe pivotsConstructing pandas DataFrame from values in variables gives “ValueError: If using all scalar values, you must pass an index”Stack and Pivot Dataframe in PythonPandas: ValueError when pivoting dataframe with MultiIndexHow to pivot a set of multiple columns into a set of flagged values through DataFrame while not wanting to pivot all columnsreorder multi index dataframe with pivotPython pivot DataFrame without index columnsPython pivot dataframe valuesPivot dataframe in Python
.everyoneloves__top-leaderboard:empty,.everyoneloves__mid-leaderboard:empty,.everyoneloves__bot-mid-leaderboard:empty margin-bottom:0;
I am trying to reshape a pandas data frame that I previously melted. The problem is that in the var_name part are repeating names that I would like to have as columns.
This is how it looks like at the moment as an example:
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
| Duration_survey | Q1_gender | Q2_age | ….. | Valdidation | categories | judgements |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - First Click | 12.085 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - First Click | 10.777 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Last Click | 12.085 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Last Click | 10.777 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Page Submit | 12.899 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Page Submit | 11.906 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Click Count | 1 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Click Count | 1 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 8 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 7 |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
*please note: this a shortened version - as shown in the code below there are more columns.
And this is what I would like to have in the end based an previous example:
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
| Duration_survey | Q1_gender | Q2_age | ….. | Valdidation | categories | Timing - First Click | Timing - Last Click | Timing - Page Submit | Timing - Click Count | judgements |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
| 657 | Male | Older than 40 | | En la variedad hay placer. | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 12.085 | 12.085 | 12.899 | 1 | 8 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 10.777 | 10.777 | 11.906 | 1 | 7 |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
*also the number of tweets in the "category" column more than 1000.
I thought I could use pivot to get all tweets first as column, but Timing - First Click, Timing - Last Click, Timing - Page Submit, Timing - Click Count would get in the shape I want and then I just melt them down again while specifying those 4 Timing related columns as id_vars to remain in their shape. But I don't even get that far - pivoting doesn't work:
#first melt
df_clean = pd.melt(df,
id_vars=['Duration_survey', 'Q1_gender', 'Q2_age', 'Q3_country', 'Q4_level_of_study',
'Q4_level_of_study_other',
'Q5_native_lang', 'Q6_second_lang', 'Q7_english_test', 'Q8_english_test_name',
'Q8_english_test_name_other', 'Q9_english_test_time', 'Q10_english_test_result',
'Q11_IELTS_test_result',
'Q12_twitter_usage', 'Valdidation'], var_name='categories', value_name='judgements')
#clear for empty judgements
df_wo_na = df_clean.dropna(subset=['judgements'])
#pivot
df_p = df_wo_na.pivot(index=['Duration_survey', 'Q1_gender', 'Q2_age', 'Q3_country', 'Q4_level_of_study',
'Q4_level_of_study_other',
'Q5_native_lang', 'Q6_second_lang', 'Q7_english_test', 'Q8_english_test_name',
'Q8_english_test_name_other', 'Q9_english_test_time', 'Q10_english_test_result',
'Q11_IELTS_test_result',
'Q12_twitter_usage', 'Valdidation'], columns='categories', values='judgements')
So this is where I fail and it gets me an Error.
ValueError: Length of passed values is 5202, index implies 16
Has anyone has an idea how to solve this?
Thank you in advance
python dataframe stack pivot melt
asked Mar 25 at 22:35
Patrick JacobPatrick Jacob
33 bronze badges
add a comment |
I am trying to reshape a pandas data frame that I previously melted. The problem is that in the var_name part are repeating names that I would like to have as columns.
This is how it looks like at the moment as an example:
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
| Duration_survey | Q1_gender | Q2_age | ….. | Valdidation | categories | judgements |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - First Click | 12.085 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - First Click | 10.777 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Last Click | 12.085 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Last Click | 10.777 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Page Submit | 12.899 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Page Submit | 11.906 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Click Count | 1 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Click Count | 1 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 8 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 7 |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
*please note: this a shortened version - as shown in the code below there are more columns.
And this is what I would like to have in the end based an previous example:
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
| Duration_survey | Q1_gender | Q2_age | ….. | Valdidation | categories | Timing - First Click | Timing - Last Click | Timing - Page Submit | Timing - Click Count | judgements |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
| 657 | Male | Older than 40 | | En la variedad hay placer. | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 12.085 | 12.085 | 12.899 | 1 | 8 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 10.777 | 10.777 | 11.906 | 1 | 7 |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
*also the number of tweets in the "category" column more than 1000.
I thought I could use pivot to get all tweets first as column, but Timing - First Click, Timing - Last Click, Timing - Page Submit, Timing - Click Count would get in the shape I want and then I just melt them down again while specifying those 4 Timing related columns as id_vars to remain in their shape. But I don't even get that far - pivoting doesn't work:
#first melt
df_clean = pd.melt(df,
id_vars=['Duration_survey', 'Q1_gender', 'Q2_age', 'Q3_country', 'Q4_level_of_study',
'Q4_level_of_study_other',
'Q5_native_lang', 'Q6_second_lang', 'Q7_english_test', 'Q8_english_test_name',
'Q8_english_test_name_other', 'Q9_english_test_time', 'Q10_english_test_result',
'Q11_IELTS_test_result',
'Q12_twitter_usage', 'Valdidation'], var_name='categories', value_name='judgements')
#clear for empty judgements
df_wo_na = df_clean.dropna(subset=['judgements'])
#pivot
df_p = df_wo_na.pivot(index=['Duration_survey', 'Q1_gender', 'Q2_age', 'Q3_country', 'Q4_level_of_study',
'Q4_level_of_study_other',
'Q5_native_lang', 'Q6_second_lang', 'Q7_english_test', 'Q8_english_test_name',
'Q8_english_test_name_other', 'Q9_english_test_time', 'Q10_english_test_result',
'Q11_IELTS_test_result',
'Q12_twitter_usage', 'Valdidation'], columns='categories', values='judgements')
So this is where I fail and it gets me an Error.
ValueError: Length of passed values is 5202, index implies 16
Has anyone has an idea how to solve this?
Thank you in advance
python dataframe stack pivot melt
asked Mar 25 at 22:35
Patrick JacobPatrick Jacob
33 bronze badges
add a comment |
I am trying to reshape a pandas data frame that I previously melted. The problem is that in the var_name part are repeating names that I would like to have as columns.
This is how it looks like at the moment as an example:
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
| Duration_survey | Q1_gender | Q2_age | ….. | Valdidation | categories | judgements |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - First Click | 12.085 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - First Click | 10.777 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Last Click | 12.085 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Last Click | 10.777 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Page Submit | 12.899 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Page Submit | 11.906 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Click Count | 1 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Click Count | 1 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 8 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 7 |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
*please note: this a shortened version - as shown in the code below there are more columns.
And this is what I would like to have in the end based an previous example:
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
| Duration_survey | Q1_gender | Q2_age | ….. | Valdidation | categories | Timing - First Click | Timing - Last Click | Timing - Page Submit | Timing - Click Count | judgements |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
| 657 | Male | Older than 40 | | En la variedad hay placer. | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 12.085 | 12.085 | 12.899 | 1 | 8 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 10.777 | 10.777 | 11.906 | 1 | 7 |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
*also the number of tweets in the "category" column more than 1000.
I thought I could use pivot to get all tweets first as column, but Timing - First Click, Timing - Last Click, Timing - Page Submit, Timing - Click Count would get in the shape I want and then I just melt them down again while specifying those 4 Timing related columns as id_vars to remain in their shape. But I don't even get that far - pivoting doesn't work:
#first melt
df_clean = pd.melt(df,
id_vars=['Duration_survey', 'Q1_gender', 'Q2_age', 'Q3_country', 'Q4_level_of_study',
'Q4_level_of_study_other',
'Q5_native_lang', 'Q6_second_lang', 'Q7_english_test', 'Q8_english_test_name',
'Q8_english_test_name_other', 'Q9_english_test_time', 'Q10_english_test_result',
'Q11_IELTS_test_result',
'Q12_twitter_usage', 'Valdidation'], var_name='categories', value_name='judgements')
#clear for empty judgements
df_wo_na = df_clean.dropna(subset=['judgements'])
#pivot
df_p = df_wo_na.pivot(index=['Duration_survey', 'Q1_gender', 'Q2_age', 'Q3_country', 'Q4_level_of_study',
'Q4_level_of_study_other',
'Q5_native_lang', 'Q6_second_lang', 'Q7_english_test', 'Q8_english_test_name',
'Q8_english_test_name_other', 'Q9_english_test_time', 'Q10_english_test_result',
'Q11_IELTS_test_result',
'Q12_twitter_usage', 'Valdidation'], columns='categories', values='judgements')
So this is where I fail and it gets me an Error.
ValueError: Length of passed values is 5202, index implies 16
Has anyone has an idea how to solve this?
Thank you in advance
python dataframe stack pivot melt
asked Mar 25 at 22:35
Patrick JacobPatrick Jacob
33 bronze badges
I am trying to reshape a pandas data frame that I previously melted. The problem is that in the var_name part are repeating names that I would like to have as columns.
This is how it looks like at the moment as an example:
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
| Duration_survey | Q1_gender | Q2_age | ….. | Valdidation | categories | judgements |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - First Click | 12.085 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - First Click | 10.777 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Last Click | 12.085 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Last Click | 10.777 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Page Submit | 12.899 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Page Submit | 11.906 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Timing - Click Count | 1 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Timing - Click Count | 1 |
| 657 | Male | Older than 40 | | En la variedad hay placer. | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 8 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 7 |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+------------+
*please note: this a shortened version - as shown in the code below there are more columns.
And this is what I would like to have in the end based an previous example:
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
| Duration_survey | Q1_gender | Q2_age | ….. | Valdidation | categories | Timing - First Click | Timing - Last Click | Timing - Page Submit | Timing - Click Count | judgements |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
| 657 | Male | Older than 40 | | En la variedad hay placer. | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 12.085 | 12.085 | 12.899 | 1 | 8 |
| 480 | Male | 31-40 | | en la variedad esta el placer | Anyways, despite the urgency it’s fraught. Just check out media #twitter’s reaction to the ambiguity around who gets to spend this money. #cdnmedia #journalism | 10.777 | 10.777 | 11.906 | 1 | 7 |
+-----------------+-----------+---------------+-----+-------------------------------+-----------------------------------------------------------------------------------------------------------------------------------------------------------------+----------------------+---------------------+----------------------+----------------------+------------+
*also the number of tweets in the "category" column more than 1000.
I thought I could use pivot to get all tweets first as column, but Timing - First Click, Timing - Last Click, Timing - Page Submit, Timing - Click Count would get in the shape I want and then I just melt them down again while specifying those 4 Timing related columns as id_vars to remain in their shape. But I don't even get that far - pivoting doesn't work:
#first melt
df_clean = pd.melt(df,
id_vars=['Duration_survey', 'Q1_gender', 'Q2_age', 'Q3_country', 'Q4_level_of_study',
'Q4_level_of_study_other',
'Q5_native_lang', 'Q6_second_lang', 'Q7_english_test', 'Q8_english_test_name',
'Q8_english_test_name_other', 'Q9_english_test_time', 'Q10_english_test_result',
'Q11_IELTS_test_result',
'Q12_twitter_usage', 'Valdidation'], var_name='categories', value_name='judgements')
#clear for empty judgements
df_wo_na = df_clean.dropna(subset=['judgements'])
#pivot
df_p = df_wo_na.pivot(index=['Duration_survey', 'Q1_gender', 'Q2_age', 'Q3_country', 'Q4_level_of_study',
'Q4_level_of_study_other',
'Q5_native_lang', 'Q6_second_lang', 'Q7_english_test', 'Q8_english_test_name',
'Q8_english_test_name_other', 'Q9_english_test_time', 'Q10_english_test_result',
'Q11_IELTS_test_result',
'Q12_twitter_usage', 'Valdidation'], columns='categories', values='judgements')
So this is where I fail and it gets me an Error.
ValueError: Length of passed values is 5202, index implies 16
Has anyone has an idea how to solve this?
Thank you in advance
python dataframe stack pivot melt
python dataframe stack pivot melt
asked Mar 25 at 22:35
Patrick JacobPatrick Jacob
33 bronze badges
asked Mar 25 at 22:35
Patrick JacobPatrick Jacob
33 bronze badges
asked Mar 25 at 22:35
Patrick JacobPatrick Jacob
33 bronze badges
asked Mar 25 at 22:35
Patrick JacobPatrick Jacob
33 bronze badges
asked Mar 25 at 22:35
Patrick JacobPatrick Jacob
33 bronze badges
33 bronze badges
add a comment |
add a comment |
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