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Dataset:
Need output like below in using dataframe pandas. I would like to group by PRCP based on the PRCP range and aggregate the count. Please advise
import pandas as pd
df = pd.DataFrame({'CLDATE':['1/1/16','1/10/16','1/11/16','11/12/16','11/13/16','11/14/16','11/15/16','11/16/16'],
'count':[64396,49877,41603,41124,45839,45846,52719,59626],'PRCP':[0,1.8,0,0,0,0,0,0.24]})
df['precipate_Range']=pd.cut(df['PRCP'],[0,1,2,3],right=False,labels=['0-1','1-2','2-3'])
df.groupby('precipate_Range')['count'].agg({'Sum':'sum'}).reset_index()
Output:
precipate_Range Sum
0 0-1 351153.0
1 1-2 49877.0
2 2-3 NaN
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there is one dataframe with columns A and B, each value in A has many values in B,
i want to perform few steps on rows for each of the value in A.
could you help to do it in spark
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Is there any nice build in function in pandas which can convert values in DataFrame to percent (by rows, by columns or any more complex combination with use of levels)?
Im not sure that is what you mean, but if you want to know the percentage value within the columns you can do it like this:
for col in df:
df[col] = (df[col]/df[col].sum()) * 100
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I am a python newb having only learnt in last 3 month.
I have a .csv file with +1000columns of data.
Each row is related to a specific data series with each column a row.
I am struggling to define each data series within my code - and then column series as well - though I feel this will be easier?
This is my most complex code I have written so I am both very excited whilst doing it but running into problems with my understanding.
I suggest you start with loading your csv with pandas using
import pandas as pd
pd.read_csv('filename.csv')
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I'm trying to count the columns that have the same name. I need a formula to apply it to all data.
For example, I am trying to count the amount of cloumns with ID=11 and write them in row "N".
Is there a simple formula for this?
Try using COUNTIF:
=COUNTIF(A3:A25, 11)
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I am unable to get sentiment for all the rows in the dataframe. Able to see sentiment for only one row.
text = doc.text[0]
This statement only brings 1st row. Hence, you see only 1st row in your answerset.
For getting sentiments for all rows, run a loop:
for index, row in doc.iterrows():
text = row[0]
obj = TextBlob(text)
sentiment = obj.sentiment.polarity
print(sentiment)
Let me know if it works.