I have a dataframe with hundreds of columns (stocks). My issue is that I need to always pull a specific column (date) followed by an array/list of others (dynamic).
Previously I was doing something like this:
df = stocks[['date', 'AAPL', 'AMZN']]
but now if I need to dynamically choose stocks based on a sector I am not sure how to make these play nice together. I am only able to pull the list without using date like this:
print(rowData['symbol'])
3 [APA.OQ, BKR.N, COG.N, CVX.N, CXO.N, COP.N, DV...
Name: symbol, dtype: object
selection = rowData['symbol'].explode()
df = stocks[selection]
how do I also get the date values? something like this doesn't work:
df = stocks[['date'][selection]]
Thanks
Let us try
df = stocks[['date'] + rowData['symbol'].iloc[0]]
Related
I am working with a dataframe and one of the columns has for values a list of strings in each row. The list contains a number of links (each list can have a different number of links). I want to create a new column that will be based on this column of lists but keep only the links that have the keyword "uploads".
To my example, the first entry of the column is like that:
['https://seekingalpha.com/instablog/5006891-hfir/4960045-natural-gas-daily',
'https://seekingalpha.com/article/4116929-weekly-natural-gas-storage-report',
'https://static.seekingalpha.com/uploads/2017/10/26/5006891-15090647719993095_origin.png',
'https://static.seekingalpha.com/uploads/2017/10/26/5006891-15090647854075453_origin.png',
'https://static.seekingalpha.com/uploads/2017/10/26/5006891-1509065004154725_origin.png',
'https://seekingalpha.com/account/research/subscribe?slug=hfir-energy&sasource=upsell']
And I want to keep only
['https://static.seekingalpha.com/uploads/2017/10/26/5006891-15090647719993095_origin.png',
'https://static.seekingalpha.com/uploads/2017/10/26/5006891-15090647854075453_origin.png',
'https://static.seekingalpha.com/uploads/2017/10/26/5006891-1509065004154725_origin.png']
And put the clean version in a new column of the same dataframe.
Can you please suggest a way to do it?
I just found a way where I create a function that looks within a list for a specific pattern (in my case the keyword "uploads")
def clean_alt_list(list_):
list_ = [s for s in list_ if "uploads" in s]
return list_
And then I apply this function into the column I am interested in
df['clean_links'] = df['links'].apply(clean_alt_list)
IIUC, this should work for you:
df = pd.DataFrame({'url': [['https://seekingalpha.com/instablog/5006891-hfir/4960045-natural-gas-daily', 'https://seekingalpha.com/article/4116929-weekly-natural-gas-storage-report', 'https://static.seekingalpha.com/uploads/2017/10/26/5006891-15090647719993095_origin.png', 'https://static.seekingalpha.com/uploads/2017/10/26/5006891-15090647854075453_origin.png', 'https://static.seekingalpha.com/uploads/2017/10/26/5006891-1509065004154725_origin.png', 'https://seekingalpha.com/account/research/subscribe?slug=hfir-energy&sasource=upsell']]})
df = df.explode('url').reset_index(drop=True)
df[df['url'].str.contains('uploads')]
Result:
url
2 https://static.seekingalpha.com/uploads/2017/1...
3 https://static.seekingalpha.com/uploads/2017/1...
4 https://static.seekingalpha.com/uploads/2017/1...
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
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.
I have two dataframes:
Dataframe #1
Reads the values--Will only be interested in NodeID AND GSE
sta = pd.read_csv(filename)
Dataframe #2
Reads the file, use pivot and get the following result
sim = pd.read_csv(headout,index_col=0)
sim['Layer'] = sim.groupby('date').cumcount() + 1
sim['Layer'] = 'L' + sim['Layer'].astype(str)
sim = sim.pivot(index = None , columns = 'Layer').T
This gives me the index column to be with two values. (The header is blank for the first one, and Layers for the second) i.e 1,L1.
What I need help on is:
I can not find a way to rename that first blank in the index to 'NodeID'.
I want to name it that so that I can do the lookup function and use NodeID in both dataframes so that I can bring in the 'GSE' values from the first dataframe to the second.
I have been googling way to rename that first column in the second dataframe and I can not seem to find an solution. Any ideas help at this point. I think my pivot function might be wrong...
This is a picture of dataframe #2 before pivot. The number 1-4 are the Node ID.
when I export it to csv to see what the dataframe looks like I get this..
Try
df.rename(columns={"Index": "your preferred name"})
if it is your index then do -
df = df.reset_index()
df.rename(columns={"index": "your preferred name"})
I have a pandas Data Frame where some of the id's are repeated a few times. I've written this code:
df = df["id"].value_counts()
and got this output
What should I do to get something like in the following image?
Thanks
As Quang Hoang answered, value_counts set the column you count as the index. Therefore in order to get the id and the count as columns, you need to do 2 things:
Make the counts as column - to_frame(name='B')
Reset the index to make the ids another column which we'll rename to the desired name: .reset_index().rename(columns={'index': 'A'})
So in one line it'll be:
df = df["id"].value_counts().to_frame(name='B').reset_index().rename(columns={'index': 'A'})
Another possible way is:
col = list(["A", "B")]
df.columns = col