I have a dataframe column such as below.
{"urls":{"web":{"discover":"http://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20film"}},"color":16734574,"parent_id":11,"name":"Narrative Film","id":31,"position":13,"slug":"film & video/narrative film"}
I want to extract the info against the word 'slug'. (In this instance it is film & video/narrative film) and store the info as a new dataframe column.
How can I do this ?
Many thanks
This is a (nested) dictionary with different kinds of entries, so it does not make much sense to treat it as a DataFrame column. You could treat it as a DataFrame row, with the dictionary keys giving the column names:
import pandas as pd
dict = {"urls":{"web":{"discover":"http://www.kickstarter.com/discover/categories/film%20&%20video/narrative%20film"}},
"color":16734574, "parent_id":11, "name":"Narrative Film", "id":31, "position":13,
"slug":"film & video/narrative film"}
df = pd.DataFrame(dict, index=[0])
display(df)
Output:
urls color parent_id name id position slug
0 NaN 16734574 11 Narrative Film 31 13 film & video/narrative film
Note that the urls entry is not recognized, due to the sub-dictionary.
In any case, this does yield slug as a column, so please let me know if this answers your question.
Of course you could also extract the slug entry directly from your dictionary:
dict['slug']
Related
I am trying to convert a list of dicts with the following format to a single Dataframe where each row contains the a specific type of betting odds offered by one sports book (meaning ‘h2h’ odds and ‘spread’ odds are in separate rows):
temp = [{"id":"e4cb60c1cd96813bbf67450007cb2a10",
"sport_key":"americanfootball",
"sport_title":"NFL",
"commence_time":"2022-11-15T01:15:31Z",
"home_team":"Philadelphia Eagles",
"away_team":"Washington Commanders",
"bookmakers":
[{"key":"fanduel","title":"FanDuel",
"last_update":"2022-11-15T04:00:35Z",
"markets":[{"key":"h2h","outcomes":[{"name":"Philadelphia
Eagles","price":630},{"name":"Washington Commanders","price":-1200}]}]},
{"key":"draftkings","title":"DraftKings",
"last_update":"2022-11-15T04:00:30Z",
"markets":[{"key":"h2h","outcomes":[{"name":"Philadelphia Eagles","price":600},
{"name":"Washington Commanders","price":-950}]}]},
There are many more bookmaker entries of the same format. I have tried:
df = pd.DataFrame(temp)
# normalize the column of dicts
normalized = pd.json_normalize(df['bookmakers'])
# join the normalized column to df
df = df.join(normalized,).drop(columns=['bookmakers'])
# join the normalized column to df
df = df.join(normalized, lsuffix = 'key')
However, this results in a Dataframe with repeated columns and columns that contain dictionaries.
Thanks for any help in advance!
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 am extracting tables from pdf using Camelot. Two of the columns are getting merged together with a newline separator. Is there a way to separate them into two columns?
Suppose the column looks like this.
A\nB
1\n2
2\n3
3\n4
Desired output:
|A|B|
|-|-|
|1|2|
|2|3|
|3|4|
I have tried df['A\nB'].str.split('\n', 2, expand=True) and that splits it into two columns however I want the new column names to be A and B and not 0 and 1. Also I need to pass a generalized column label instead of actual column name since I need to implement this for several docs which may have different column names. I can determine such column name in my dataframe using
colNew = df.columns[df.columns.str.contains(pat = '\n')]
However when I pass colNew in split function, it throws an attribute error
df[colNew].str.split('\n', 2, expand=True)
AttributeError: DataFrame object has no attribute 'str'
You can take advantage of the Pandas split function.
import pandas as pd
# recreate your pandas series above.
df = pd.DataFrame({'A\nB':['1\n2','2\n3','3\n4']})
# first: Turn the col into str.
# second. split the col based on seperator \n
# third: make sure expand as True since you want the after split col become two new col
test = df['A\nB'].astype('str').str.split('\n',expand=True)
# some rename
test.columns = ['A','B']
I hope this is helpful.
I reproduced the error from my side... I guess the issue is that "df[colNew]" is still a dataframe as it contains the indexes.
But .str.split() only works on Series. So taking as example your code, I would convert the dataframe to series using iloc[:,0].
Then another line to split the column headers:
df2=df[colNew].iloc[:,0].str.split('\n', 2, expand=True)
df2.columns = 'A\nB'.split('\n')
I have a csv file with 4 columns (Name, User_Name, Phone#, Email"). I want to delete those rows which have none value either in Phone# or Email. If there is none value in column (Phone#) and have some value in column(Email)or vise versa I don't want to delete that column. I hope you people will get what I want.
Sorry I don't have the code.
Thanks in advance
You can use the pandas notna() function to get a boolean series indicating which values are not missing. You can call this on both the email and the phone column and combine it with boolean | to get a truth series indicating that at least one of the email and phone columns is not missing. Then, you can use this series as a mask to filter the right rows.
import pandas as pd
# Import .csv file
df = pd.read_csv('mypath/myfile.csv')
# Filter to get rows where columns 'Email' and 'Phone#' are not both None
new_df = df[df['Email'].notna() | df['Phone#'].notna()]
# Write pandas df to disk
new_df.to_csv('mypath/mynewfile.csv', index=False)
I used this code
unclassified_df['COUNT'] = unclassified_df.tweet.str.count('mulcair')
to count the number of times mulcair appeared in each row in my pandas dataframe. I am trying to repeat the same but for a set of words such as
Liberal = ['lpc','ptlib','justin','trudeau','realchange','liberal', 'liberals', "liberal2015",'lib2015','justin2015', 'trudeau2015', 'lpc2015']
I saw somewhere that I could use collection.Counter(data) and its .most_common(k) method for such, please can anyone help me out.
from collections import Counter
import pandas as pd
#check frequency for the following for each row, but no repetition for row
Liberal = ['lpc','justin','trudeau','realchange','liberal', 'liberals', "liberal2015", 'lib2015','justin2015', 'trudeau2015', 'lpc2015']
#sample data
data = {'tweet': ['lpc living dream camerama', "jsutingnasndsa dnsadnsadnsa dsalpcdnsa", "but", 'mulcair suggests thereslcp bad lpc blood']}
# the data frame with one coloumn tweet
df = pd.DataFrame(data,columns=['tweet'])
#no duplicates per row
print [(df.tweet.str.contains(word).sum(),word) for word in Liberal]
#captures all duplicates located in each row
print pd.Series({w: df.tweet.str.count(w).sum() for w in Liberal})
References:
Contains & match