I'm having trouble merging two dataframes in pandas. They are parts of a dataset split between two files, and they share some columns and values, namely 'name' and 'address'. The entries with identical values do not share their index with entries in the other file. I tried variations of the following line:
res = pd.merge(df, df_p, on=['name', 'address'], how="left")
When the how argument was set to 'left', the columns from df_p had no values. 'right' had the opposite effect, with columns from df being empty. 'inner' resulted in an empty dataframe and 'outer' duplicated the number of entries, essentially just appending the results of 'left' and 'right'.
I manually verified that there are identical combinations of 'name' and 'address' values in both files.
Edit: Attempt at merging on a single of those columns appears to be successful, however I want to avoid merging incorrect entries in case 2 people with identical names have different addresses and vice versa
Edit1: Here's some more information on the data-set.
df.info() output:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3983 entries, 0 to 3982
Data columns (total 23 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 3983 non-null int64
1 name 3983 non-null object
2 address 3983 non-null object
3 race 3970 non-null object
4 marital-status 3967 non-null object
5 occupation 3971 non-null object
6 pregnant 3969 non-null object
7 education-num 3965 non-null float64
8 relationship 3968 non-null object
9 skewness_glucose 3972 non-null float64
10 mean_glucose 3572 non-null float64
11 capital-gain 3972 non-null float64
12 kurtosis_glucose 3970 non-null float64
13 education 3968 non-null object
14 fnlwgt 3968 non-null float64
15 class 3969 non-null float64
16 std_glucose 3965 non-null float64
17 income 3974 non-null object
18 medical_info 3968 non-null object
19 native-country 3711 non-null object
20 hours-per-week 3971 non-null float64
21 capital-loss 3969 non-null float64
22 workclass 3968 non-null object
dtypes: float64(10), int64(1), object(12)
memory usage: 715.8+ KB
example entry from df:
0,Curtis Brown,"32266 Byrd Island
Fowlertown, DC 84201", White, Married-civ-spouse, Exec-managerial,f,9.0, Husband,1.904881822,79.484375,15024.0,0.667177618, HS-grad,147707.0,0.0,39.49544760000001, >50K,"{'mean_oxygen':'1.501672241','std_oxygen':'13.33605383','kurtosis_oxygen':'11.36579476','skewness_oxygen':'156.77910559999995'}", United-States,60.0,0.0, Private
df_p.info() output:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 3933 entries, 0 to 3932
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 3933 non-null int64
1 name 3933 non-null object
2 address 3933 non-null object
3 age 3933 non-null int64
4 sex 3933 non-null object
5 date_of_birth 3933 non-null object
dtypes: int64(2), object(4)
memory usage: 184.5+ KB
sample entry from df_p:
2273,Curtis Brown,"32266 Byrd Island
Fowlertown, DC 84201",44, Male,1975-03-26
As you can see, the chosen samples are for the same person, but their index does not match, which is why I tried using the name and address columns.
Edit2: Changing the order of df and df_p in the merge seems to have solved the issue, though I have no clue why.
Related
I am trying to create a new pandas dataframe displayDF with 4 columns from the dataframe finalDF.
displayDF = finalDF[['False','True','RULE ID','RULE NAME']]
This command is failing with the error:
KeyError: "['False', 'True'] not in index"
However, I can see the columns "False" and "True" when I run finalDF.info()
<class 'pandas.core.frame.DataFrame'>
Int64Index: 12 entries, 0 to 11
Data columns (total 6 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 rule_rec_id 12 non-null object
1 False 12 non-null int64
2 True 12 non-null int64
3 RULE ID 12 non-null object
4 RULE NAME 12 non-null object
5 RULE DESCRIPTION 12 non-null object
dtypes: int64(2), object(4)
memory usage: 672.0+ bytes
Additional Background:
I created finalDF by merging two dataframes (pivot_stackedPandasDF and dfPandaDescriptions)
finalDF = pd.merge(pivot_stackedPandasDF, dfPandaDescriptions, how='left', left_on=['rule_rec_id'], right_on=['RULE ID'])
I created pivot_stackedPandasDF with this command.
pivot_stackedPandasDF = stackedPandasDF.pivot_table(index="rule_rec_id", columns="alert_value", values="count").reset_index()
I think the root cause may be in the way I ran the .pivot_table() command.
I'm Using Mac. In my mac I Install Anaconda. I used Jupiter notebook 6.1.4 in this to work on data. For Learning purpose, I'm using Kaggle SF Salaries Dataset(https://www.kaggle.com/kaggle/sf-salaries).
After Importing the file in Jupyter Notebook & using the command df.info() it is showing specifications like this
>>>><class 'pandas.core.frame.DataFrame'>
RangeIndex: 148654 entries, 0 to 148653
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Id 148654 non-null int64
1 EmployeeName 148654 non-null object
2 JobTitle 148654 non-null object
3 BasePay 148049 non-null object
4 OvertimePay 148654 non-null object
5 OtherPay 148654 non-null object
6 Benefits 112495 non-null object
7 TotalPay 148654 non-null float64
8 TotalPayBenefits 148654 non-null float64
9 Year 148654 non-null int64
10 Notes 0 non-null float64
11 Agency 148654 non-null object
12 Status 38119 non-null object
dtypes: float64(3), int64(2), object(8)
memory usage: 14.7+ MB.
In the environment of colab same data set is showing different specifications.
>>>>>>>><class 'pandas.core.frame.DataFrame'>
RangeIndex: 116475 entries, 0 to 116474
Data columns (total 13 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Id 116475 non-null int64
1 EmployeeName 116475 non-null object
2 JobTitle 116475 non-null object
3 BasePay 115870 non-null float64
4 OvertimePay 116474 non-null float64
5 OtherPay 116474 non-null float64
6 Benefits 80315 non-null float64
7 TotalPay 116474 non-null float64
8 TotalPayBenefits 116474 non-null float64
9 Year 116474 non-null float64
10 Notes 0 non-null float64
11 Agency 116474 non-null object
12 Status 5943 non-null object
dtypes: float64(8), int64(1), object(4)
memory usage: 11.6+ MB.
enter image description here
The dataset is a csv file. The csv format is a plain text format: one line per row (normally delimited with a '\r\n'), each line containing fields separated with a delimiter (normally the comma ','), and optionaly enclosed in quotes.
But there is no indication for the datatypes. Dumb tools (text editors or LibreOffice calc) present the raw data to the user, so that the user may choose the datatypes, delimiters and encoding. Clever tools (Excel and in some sense Collab or Pandas) think that they can guess everything, either because they decide from what they think common or with some heuristics. So there is no surprise that they end with different guesses.
(If you have not guessed it, I hate Excel handling of csv files, and only rely on calc...)
I merge three dataframes, but the result displayed is different than the actual result. I want the result displayed.
Here is the merge code:
df_twitter_archive_clean_test.merge(df_tweets_clean_test, on='tweet_id', how='left')
df_twitter_archive_clean_test.merge(df_images_clean_test, on='tweet_id')
Here is part of the result that pops up after running this code:
tweet_id in_reply_to_status_id in_reply_to_user_id timestamp source text retweeted_status_id retweeted_status_user_id retweeted_status_timestamp expanded_urls rating_numerator rating_denominator name doggo floofer pupper puppo jpg_url img_num p1 p1_conf p1_dog p2 p2_conf p2_dog p3 p3_conf p3_dog
That result has 28 columns.
But when I run df_twitter_archive_clean_test.info() I get 17 columns!
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 2356 entries, 0 to 2355
Data columns (total 17 columns):
tweet_id 2356 non-null object
in_reply_to_status_id 78 non-null float64
in_reply_to_user_id 78 non-null float64
timestamp 2356 non-null object
source 2356 non-null object
text 2356 non-null object
retweeted_status_id 181 non-null float64
retweeted_status_user_id 181 non-null float64
retweeted_status_timestamp 181 non-null object
expanded_urls 2297 non-null object
rating_numerator 2356 non-null int64
rating_denominator 2356 non-null int64
name 2356 non-null object
doggo 2356 non-null object
floofer 2356 non-null object
pupper 2356 non-null object
puppo 2356 non-null object
dtypes: float64(4), int64(2), object(11)
memory usage: 313.0+ KB
Testing the data reveals that the dataset has 17 columns.
How can I stop this mysterious change?
I assume you are not copying or defining a new variable after the merge therefore you get the information df_twitter_archive_clean_test pre-merge. This can sometimes be solved with an extra parameter called in_place which basically defines if after applying a function to an existing dataframe, changes will be saved in that dataframe, or if assigning the changes to a new dataframe must be done to keep the changes.
If you wish to solve this you can try:
semi_merged_df = df_twitter_archive_clean_test.merge(df_tweets_clean_test, on='tweet_id', how='left')
merged_df = semi_merged_df.merge(df_images_clean_test, on='tweet_id')
And finally
print(merged_df.info())
Should return your expected output.
Changing the merge code to this seems to get of the problem:
df_master = pd.merge(df_twitter_archive_clean_test, df_images_clean_test, how = 'left', on = ['tweet_id'] )
df_master = pd.merge(df_master, df_tweets_clean_test, how = 'left', on = ['tweet_id'])
I don't know why,
I have two dataframes with the following info:
>>> ratings.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 5 entries, 0 to 4
Data columns (total 4 columns):
id 5 non-null int64
movie_id 5 non-null object
rating 5 non-null object
account_id 5 non-null int64
dtypes: int64(2), object(2)
memory usage: 240.0+ bytes
>> movies.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 296 entries, 0 to 295
Data columns (total 9 columns):
id 296 non-null int64
description 296 non-null object
genre 296 non-null object
imdb_url 296 non-null object
img_url 296 non-null object
title 296 non-null object
users_rating 296 non-null object
year 296 non-null object
movie_id 296 non-null object
dtypes: int64(1), object(8)
memory usage: 20.9+ KB
Inspite of the common columns having the same data types, it shows:
>>> pd.merge(ratings,movies)
Empty DataFrame
Columns: [id, movie_id, rating, account_id, description, genre,
imdb_url, img_url, title, users_rating, year]
Index: []
The previous answers on stackoverflow suggest to check for the similarity of data types. However, since my data types are same, what is the solution for this error?
This is doing and inner join using ['id', 'movie_id'] so if the resulting DF is empty then the combinations of id & movie_id in both dataframes don't have any match. Compare the distinct 'id' and 'movie_id' combinations in both dataframes
movies.groupby(['id', 'movie_id'])['id'].count()
ratings.groupby(['id', 'movie_id'])['id'].count()
I need to compare codes in two dataframes. I'm using Python 3 and pandas
In the first base the codes always have 18 digits:
dividas_dep = pd.read_csv("dividas_deputados_ajustado_csv.csv",sep=';',encoding = 'latin_1')
dividas_dep.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 106 entries, 0 to 105
Data columns (total 10 columns):
CPF_Deputado 106 non-null object
CPF_limpo 106 non-null int64
Nome_Deputado 106 non-null object
Vinculo 106 non-null object
CNPJ_Devedor 106 non-null object
CNPJ_limpo 106 non-null int64
Nome_Devedor 106 non-null object
Valores_situacao_Irregular 65 non-null object
Valores_situacao_Regular 52 non-null object
Total_Devido 106 non-null object
dtypes: int64(2), object(8)
memory usage: 8.4+ KB
The column to compare in this first base ("CNPJ_Devedor") has these examples: 17.080.201/0001-49, 76.205.723/0001-99, 04.885.828/0001-25...
And in the second base, the codes always have 10 digits:
funrural = pd.read_excel('DEVEDORES FUNRURAL ATUALIZADO PGFN.xlsx')
funrural.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 8130 entries, 0 to 8129
Data columns (total 14 columns):
PSFN_PGFN 8129 non-null object
Regiao 8129 non-null object
CNPJ_CEI_Tipo 8129 non-null object
CNPJ_Raiz 8129 non-null object
Razao_Social 8130 non-null object
Valor_principal 8130 non-null float64
Valor_TR_IPC_Poup 8130 non-null float64
Valor_Juros 8130 non-null float64
Valor_SELIC 8130 non-null float64
Valor_Encargo 8130 non-null float64
Valor_Multa_Oficio 8130 non-null float64
Valor_Selic_M_Oficio 8130 non-null float64
Vl_Multa_Mora 8130 non-null float64
Vl_Tot_Credito 8130 non-null float64
dtypes: float64(9), object(5)
memory usage: 889.3+ KB
The column to compare in this second base ("CNPJ_Raiz") has these examples: 04.244.173, 05.006.407, 03.632.132...
The codes "CNPJ_Devedor" and "CNPJ_Raiz" are related in tax legislation, but I can not make a simple merge like this:
compara1 = pd.merge(dividas_dep, funrural, left_on='CNPJ_Devedor', right_on='CNPJ_Raiz')
What I need to do is compare only the first 10 digits of "CNPJ_Devedor" with the code "CNPJ_Raiz" (example, in "17.080.201/0001-49" use only "17.080.201")
Is there any way to do this in Python? Or should I edit the original dataframe file, dividas_dep (dividas_deputados_ajustado_csv.csv), to create a new column with only the first 10 digits?
You can compare the slice of the first 10 string elements with .str.slice(None, 10):
dividas_dep["CNPJ_Devedor"].str.slice(None, 10) == funrural["CNPJ_Raiz"]
Example:
>>> dividas_dep = pd.DataFrame({"CNPJ_Devedor": ['17.080.201/0001-49', '76.205.723/0001-99', '04.885.828/0001-25']})
>>> funrural = pd.DataFrame({"CNPJ_Raiz": ['17.080.201', '04.244.173', '05.006.407']})
>>> dividas_dep["CNPJ_Devedor"].str.slice(None, 10) == funrural["CNPJ_Raiz"]
0 True
1 False
2 False
dtype: bool
You can use the result to create a new dataframe:
res = dividas_dep["CNPJ_Devedor"].str.slice(None, 10) == funrural["CNPJ_Raiz"]
funrural[res]