I have tried merging with Pandas merge, however, as the length of data is different, merge function is broadcasting the data even when using a key.
The following line of code has been used.
dt = pd.merge(df,data[['Post ID','Sentiment']], on = 'Post ID')
Using join produces the following:
df.join(data[['Post ID','Sentiment']],on = 'Post ID')
You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat
This error means that in one of your database, Post ID is an objectand in the other one it is defined as int.
You need to convert them so they have the same type, for instance by doing :
df['Post ID'] = df['Post ID'].astype(int)
Related
I have a data frame with rows that are mostly translations of other rows e.g. an English row and an Arabic row. They share an identifier (location_shelfLocator) and I'm trying to merge the rows together based on the identifier match. In some columns the Arabic doesn't contain a translation, but the same English value (e.g. for the language column both records might have ['ger'] which becomes ['ger', 'ger']) so I would like to get rid of these duplicate values. This is my code:
df_merged = df_filled.groupby("location_shelfLocator").agg(
lambda x: np.unique(x.tolist())
)
It works when the values being aggregated are the same type (e.g. when they are both strings or when they are both arrays). When one is a string and the other is an array, it doesn't work. I get this warning:
FutureWarning: ['subject_name_namePart'] did not aggregate successfully. If any error is raised this will raise in a future version of pandas. Drop these columns/ops to avoid this warning.
df_merged = df_filled.groupby("location_shelfLocator").agg(lambda x: np.unique(x.tolist()))
and the offending column is removed from the final data frame. Any idea how I can combine these values and remove duplicates when they are both lists, both strings, or one of each?
Here is some sample data:
location_shelfLocator,language_languageTerm,subject_topic,accessCondition,subject_name_namePart
81055/vdc_100000000094.0x000093,ara,"['فلك، العرب', 'فلك، اليونان', 'فلك، العصور الوسطى', 'الكواكب']",المُلكية العامة,كلاوديوس بطلميوس (بطليمو)
81055/vdc_100000000094.0x000093,ara,"['Astronomy, Arab', 'Astronomy, Greek', 'Astronomy, Medieval', 'Constellations']",Public Domain,"['Claudius Ptolemaeus (Ptolemy)', ""'Abd al-Raḥmān ibn 'Umar Ṣūfī""]"
And expected output:
location_shelfLocator,language_languageTerm,subject_topic,accessCondition,subject_name_namePart
"[‘81055/vdc_100000000094.0x000093’] ",[‘ara’],"['فلك، العرب', 'فلك، اليونان', 'فلك، العصور الوسطى', ‘الكواكب’, 'Astronomy, Arab', 'Astronomy, Greek', 'Astronomy, Medieval', 'Constellations']","[‘المُلكية العامة’, ‘Public Domain’]","[‘كلاوديوس بطلميوس (بطليمو)’,’Claudius Ptolemaeus (Ptolemy)', ""'Abd al-Raḥmān ibn 'Umar Ṣūfī""]"
If you cannot have a control over the input value, you need to fix it somehow.
Something like this. Here, I am converting string value in subject_name_namePart to array of string.
from ast import literal_eval
mask = df.subject_name_namePart.str[0] != '['
df.loc[mask, 'subject_name_namePart'] = "['" + df.loc[mask, 'subject_name_namePart'] + "']"
df['subject_name_namePart'] = df.subject_name_namePart.transform(literal_eval)
Then, you can do (explode) + aggregation.
df = df.explode('subject_name_namePart')
df = df.groupby('location_shelfLocator').agg(lambda x: x.unique().tolist())
I have a list of strings and my dataframe has several columns that i need to search (each of type object).
I need to return all rows where any of the selected columns have any of the string items within them, or is part of the string.
How do i check if 4 columns in my dataframe has any one of the items in the list of strings? The string inside the column may have part of the string provided in the list object, but probably wont have it all.
Ive tried 'list' both as a tuple and as a python list:
list = ("25110", "25910", "25990", "30110", "33110", "43999")
new_df = df.loc[(df['column1'].isin(list))
| (df['column2'].isin(list))
| (df['column3'].isin(list))
| (df['column4'].isin(list))]
When i run new_df.shape, i get (0, 12).
Im new to pandas, got a mountain of analysis to do for an intense uni project, and cant get this to work. Do i need to convert each column to be a string datatype first? (ive actually already tried THAT as well, but each datatype is still stubbornly an 'object').
IIUC:
try:
lst = ["25110", "25910", "25990", "30110", "33110", "43999"]
cols=['column1','column2','column3','column4']
Finally:
m=df[cols].astype(str).agg(lambda x:x.str.contains('|'.join(lst)),1).any(1)
#you can also use apply() in place of agg()
df[m]
#OR
df.loc[m]
I have a very large data frame that I want to split ALL of the columns except first two based on a comma delimiter. So I need to logically reference column names in a loop or some other way to split all the columns in one swoop.
In my testing of the split method:
I have been able to explicitly refer to ( i.e. HARD CODE) a single column name (rs145629793) as one of the required parameters and the result was 2 new columns as I wanted.
See python code below
HARDCODED COLUMN NAME --
df[['rs1','rs2']] = df.rs145629793.str.split(",", expand = True)
The problem:
It is not feasible to refer to the actual column names and repeat code.
I then replaced the actual column name rs145629793 with columns[2] in the split method parameter list.
It results in an ERROR
'str has ni str attribute'
You can index columns by position rather than name using iloc. For example, to get the third column:
df.iloc[:, 2]
Thus you can easily loop over the columns you need.
I know what you are asking, but it's still helpful to provide some input data and expected output data. I have included random input data in my code below, so you can just copy and paste this to run, and try to apply it to your dataframe:
import pandas as pd
your_dataframe=pd.DataFrame({'a':['1,2,3', '9,8,7'],
'b':['4,5,6', '6,5,4'],
'c':['7,8,9', '3,2,1']})
import copy
def split_cols(df):
dict_of_df = {}
cols=df.columns.to_list()
for col in cols:
key_name = 'df'+str(col)
dict_of_df[key_name] = copy.deepcopy(df)
var=df[col].str.split(',', expand=True).add_prefix(col)
df=pd.merge(df, var, how='left', left_index=True, right_index=True).drop(col, axis=1)
return df
split_cols(your_dataframe)
Essentially, in this solution you create a list of the columns that you want to loop through. Then you loop through that list and create new dataframes for each column where you run the split() function. Then you merge everything back together on the index. I also:
included a prefix of the column name, so the column names did not have duplicate names and could be more easily identifiable
dropped the old column that we did the split on.
Just import copy and use the split_cols() function that I have created and pass the name of your dataframe.
I have a pandas dateframe of two columns ['company'] which is a string and ['publication_datetime'] which is a datetime.
I want to group by company and the publication_date , adding a new column with the maximum publication_datetime for each record.
so far i have tried:
issuers = news[['company','publication_datetime']]
issuers['publication_date'] = issuers['publication_datetime'].dt.date
issuers['publication_datetime_max'] = issuers.groupby(['company','publication_date'], as_index=False)['publication_datetime'].max()
my group by does not appear to work.
i get the following error
ValueError: Wrong number of items passed 3, placement implies 1
You need the transform() method to cast the result in the original dimension of the dataframe.
issuers['max'] = issuers.groupby(['company', 'publication_date'])['publication_datetime'].transform('max')
The result of your groupby() before was returning a multi-indexed group object, which is why it's complaining about 3 values (first group, second group, and then values). But even if you just returned the values, it's combining like groups together, so you'll have fewer values than needed.
The transform() method returns the group results for each row of the dataframe in a way that makes it easy to create a new column. The returned values are an indexed Series with the indices being the original ones from the issuers dataframe.
Hope this helps! Documentation for transform here
The thing is by doing what you are doing you are trying to set a DataFrame to a column value.
Doing the following will get extract only the values without the two indexe columns:
issuers['publication_datetime_max'] = issuers.groupby(['company','publication_date'], as_index=False)['publication_datetime'].max().tolist()
Hope this help !
n1 = DataFrame({'zhanghui':[1,2,3,4] , 'wudi':[17,'gx',356,23] ,'sas'[234,51,354,123] })
n2 = DataFrame({'zhanghui_x':[1,2,3,5] , 'wudi':[17,23,'sd',23] ,'wudi_x':[17,23,'x356',23] ,'wudi_y':[17,23,'y356',23] ,'ddd':[234,51,354,123] })
code above defined two DataFrame objects. I wanna use 'zhanghui' field from n1 and 'zhanghui_x' field from n2 as "on" field merge n1 and n2,so my code like this:
n1.merge(n2,how = 'inner',left_on = 'zhanghui',right_on='zhanghui_x')
and then result columns given like this :
sas wudi_x zhanghui ddd wudi_y wudi_x wudi_y zhanghui_x
Some duplicate columns appeared,such as 'wudi_x' ,'wudi_y'.
So it's a pandas inner problems or I had a wrong usage about pd.merge ?
From pandas documentation, the merge() function has following properties;
pd.merge(left, right, how='inner', on=None, left_on=None, right_on=None,
left_index=False, right_index=False, sort=True,
suffixes=('_x', '_y'), copy=True, indicator=False,
validate=None)
where suffixes denote default suffix string to be attached to 'over-lapping' columns with defaults '_x' and '_y'.
I'm not sure if I understood your follow-up question correctly, but;
#case1
if the first dataFrame has column 'column_name_x' and the second dataFrame has column 'column_name' then there are no over-lapping columns and therefore no suffixes are attached.
#case2
if the first dataFrame has columns 'column_name', 'column_name_x' and the second dataFrame also has column 'column_name', the default suffixes attach to over-lapping columns and therefore the first frame's 'columnn_name' becomes 'column_name_x' and result in a duplicate of already existing column.
You can however, pass a None value to one(not all) of the suffixes to ensure that column names of certain dataFrame remain as-is.
Your approach is right, pandas automatically gives postscripts after merging the columns that are "duplicated" with the original headers given a postscript _x, _y, etc.
you can first select what columns to merge and proceed:
cols_to_use = n2.columns - n1.columns
n1.merge(n2[cols_to_use],how = 'inner',left_on = 'zhanghui',right_on='zhanghui_x')
result columns:
sas wudi zhanghui ddd wudi_x wudi_y zhanghui_x
When I tried to run cols_to_use = n2.columns - n1.columns,it gave me a TypeError like this:
cannot perform __sub__ with this index type: <class pandas.core.indexes.base.Index'>
then I tried to use code below:
cols_to_use = [i for i in list(n2.columns) if i not in list(n1.columns) ]
It worked fine,result columns given like this:
sas wudi zhanghui ddd wudi_x wudi_y zhanghui_x
So,#S Ringne's method really resolved my problems.
=============================================
Pandas just simply add suffix such as '_x' to resolve the duplicate-column-name problem when it comes to merging two Frame objects.
But what will it happen if the name form of 'a-column-name'+'_x' appears in either Frame object? I used to think that it will check if the name form of 'a-column-name'+'_x' appears, But actually pandas doesn't have this check?