How do I return this groupby calculated values back to the dataframe as a single column? - pandas

I am new to Pandas. Sorry for using images instead of tables here; I tried to follow the instructions for inserting a table, but I couldn't.
Pandas version: '1.3.2'
Given this dataframe with Close and Volume for stocks, I've managed to calculate OBV, using pandas, like this:
df.groupby('Ticker').apply(lambda x: (np.sign(x['Close'].diff().fillna(0)) * x['Volume']).cumsum())
The above gave me the correct values for OBV as
shown here.
However, I'm not able to assign the calculated values to a new column.
I would like to do something like this:
df['OBV'] = df.groupby('Ticker').apply(lambda x: (np.sign(x['Close'].diff().fillna(0)) * x['Volume']).cumsum())
But simply doing the expression above of course will throw us the error:
ValueError: Columns must be same length as key
What am I missing?
How can I insert the calculated values into the original dataframe as a single column, df['OBV'] ?
I've checked this thread so I'm sure I should use apply.
This discussion looked promising, but it is not for my case


Use Series.droplevel for remove first level of MultiIndex:
df['OBV'] = df.groupby('Ticker').apply(lambda x: (np.sign(x['Close'].diff().fillna(0)) * x['Volume']).cumsum()).droplevel(0)

Related

pandas: split pandas columns of unequal length list into multiple columns

I have a dataframe with one column of unequal list which I want to spilt into multiple columns (the item value will be the column names). An example is given below
I have done through iterrows, iterating thruough the rows and examine the list from each rows. It seem workable as my dataframe has few rows. However, I wonder if there is any clean methods
I have done through additional_df = pd.DataFrame(venue_df.location.values.tolist())
However the list break down into as below
thanks fro your help

Can you try this code: built assuming venue_df.location contains the list you have shown in the cells.
venue_df['school'] = venue_df.location.apply(lambda x: ('school' in x)+0)
venue_df['office'] = venue_df.location.apply(lambda x: ('office' in x)+0)
venue_df['home'] = venue_df.location.apply(lambda x: ('home' in x)+0)
venue_df['public_area'] = venue_df.location.apply(lambda x: ('public_area' in x)+0)
Hope this helps!

First lets explode your location column, so we can get your wanted end result.
s=df['Location'].explode()
Then lets use crosstab in that series so we can get your end result
import pandas as pd
pd.crosstab(s).unstack()
I didnt test it out cause i dont know you base_df

Selecting columns from a dataframe

I have a dataframe of monthly returns for 1,000 stocks with ids as column names.
monthly returns
I need to select only the columns that match the values in another dataframe which includes the ids I want.
permno list
I'm sure this is really quite simple, but I have been struggling for 2 days and if someone has an easy solution it would be so very much appreciated. Thank you.

You could convert the single-column permno list dataframe (osr_curr_permnos) into a list, and then use that list to select certain columns from your main dataframe (all_rets).
To convert the osr_curr_permnos column "0" into a list, you can use .to_list()
Then, you can use that list to slice all_rets and .copy() to make a fresh copy of it into a new dataframe.
The python code might look something like:
keep = osr_curr_permnos['0'].to_list()
selected_rets = all_rets[keep].copy()
"keep" would be a list, and "selected_rets" would be your new dataframe.
If there's a chance that osr_curr_permnos would have duplicates, you'll want to filter those out:
keep = osr_curr_permnos['0'].drop_duplicates().to_list()
selected_rets = all_rets[keep].copy()

As I expected, the answer was more simple than I was making it. Basically, I needed to take the integer values in my permnos list and recast those as strings.
osr_curr_permnos['0'] = osr_curr_permnos['0'].apply(str)
keep = osr_curr_permnos['0'].values
Then I can use that to select columns from my returns dataframe which had string values as column headers.
all_rets[keep]
It was all just a mismatch of int vs. string.

Group by multiple columns creating new column in pandas 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 !

Extracting the value from a Pandas Dataframe column that has only a unique value

Given a pandas DataFrame (df), where one column (unique_val_col) should have a unique value, what is the best the best way to extract this value (not as a list)?
So far I've used the following code:
output = list(set(df[unique_val_col)))
if len(output)==1: output = output[0]
Or if there is a chance for nans then change the first line to be:
output = [val for val in list(set(df[unique_val_col))) if val == val]
The question is whether there is a more direct way, that would also reflect the fact that the column actually has only one value without needing the 'if' statement.

I think you are trying to find a value that occurs only once, if that's so you could achieve it like this
df['unique_value_counts'].value_counts().sort_values(ascending=False).keys()[0]

pandas merge produce duplicate columns

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?