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I have a dataframe that (simplified) looks something like:
col1 col2
1 a
2 b
3 c,ddd,ee,f,5,hfsf,a
In col2, I need to be able to remove everything after the last 2 commas, and if it doesn't have commas just keep the value as is:
col1 col2
1 a
2 b
3 c,ddd,ee
again, this is simplified and the solution needs to scale up to something that has 1000's of rows, and the space between each comma will not always be the same
edit:
This is got me on the right track
df.col2 = df.col2.str.split(',').str[:2].str.join(',')
Pandas provides access to many familiar string functions, including slicing and selection, through the .str attribute:
df.col2.str.split(',').str[:3].str.join(',')
#0 a
#1 b
#2 c,ddd,ee
Related
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dframe= pd.DataFrame({'col1':['A']*3 + ['B']*4 + ['C','B','A'],'col2':[2,3,4,2,4,2,1,3,4,4]})
I want to remove duplicates from both columns and final result should look like this:
pd.DataFrame({'col1':['A'] + ['B'] + ['C'],'col2':[2,4,3]})
I tried following but the result was not as per the expectations
dframe.drop_duplicates(subset=['col1'], keep='first')
Please help.
Thanks
try:
via agg() and dropna() method:
out=dframe.agg(lambda x:pd.Series(pd.unique(x))).dropna()
OR
via apply() and dropna() method:
out=dframe.apply(lambda x:pd.Series(pd.unique(x))).dropna()
output of out:
col1 col2
0 A 2
1 B 3
2 C 4
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I am working on a dataframe. Data in the image
Q. I want the number of shows released per year but if I'm applying count() function, it's giving me 6 instead of 3. Could anyone suggest how do I get the correct value count.
To get unique value of single year, you can use
count = len(df.loc[df['release_year'] == 1945, 'show_id'].unique())
# or
count = df.loc[df['release_year'] == 1945, 'show_id'].nunique()
To summarize unique value of dataframe by year, you can drop_duplicates() on column show_id first.
df.drop_duplicates(subset=['show_id']).groupby('release_year').count()
Or use value_counts() on column after dropping duplicates.
df.drop_duplicates(subset=['show_id'])['release_year'].value_counts()
df['show_id'].nunique().count()
should do the job.
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The data is like this and it is in a data frame.
PatientId Payor
0 PAT10000 [Cash, Britam]
1 PAT10001 [Madison, Cash]
2 PAT10002 [Cash]
3 PAT10003 [Cash, Madison, Resolution]
4 PAT10004 [CIC Corporate, Cash]
I want to remove the square brackets and filter all patients who used at least a certain mode of payment eg madison then obtain their ID. Please help.
This will generate a list of tuples (id, payor). (df is the dataframe)
payment = 'Madison'
ids = [(id, df.Payor[i][1:-1]) for i, id in enumerate(df.PatientId) if payment in df.Payor[i]]
let's say, your data frame variable initialized as "df" and after removing square brackets, you want to filter all elements containing "Madison" under "Payor" column
df.replace({'[':''}, regex = True)
df.replace({']':''}, regex = True)
filteredDf = df.loc[df['Payor'].str.contains("Madison")]
print(filteredDf)
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My csv file looks like this:
5783,145v
g656,4589,3243,tt56
6579
How do I read this with pandas (or otherwise)?
(the table should contain empty cells)
You could pass a dummy separator, and then use str.split (by ",") with expand=True:
df = pd.read_csv('path/to/file.csv', sep=" ", header=None)
df = df[0].str.split(",", expand=True).fillna("")
print(df)
Output
0 1 2 3
0 5783 145v
1 g656 4589 3243 tt56
2 6579
I think that the solution proposed by #researchnewbie is good. If you need to replace the NaN values for say, zero, you could add this line after the read:
dataFrame.fillna(0, inplace=True)
Try doing the following:
import pandas as pd
dataFrame = pd.read_csv(filename)
Your empty cells should contain the NaN value, which essentially null.
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I have a dataframe df with 3 columns :
df=pd.DataFrame({
'User':['A','A','B','A','C','B','C'],
'Values':['x','y','z','p','q','r','s'],
'Date':[14,11,14,12,13,10,14]
})
I want to create a new dataframe that will contain the rows corresponding to highest values in the 'Date' columns for each user. For example for the above dataframe I want the desired dataframe to be as follows ( its a jpeg image):
Can anyone help me with this problem?
This answer assumes that there is different maximum values per user in Values column:
In [10]: def get_max(group):
...: return group[group.Date == group.Date.max()]
...:
In [12]: df.groupby('User').apply(get_max).reset_index(drop=True)
Out[12]:
Date User Values
0 14 A x
1 14 B z
2 14 C s