Hi,

I want to filter based on whether a value is in another column. However this data needs to be grouped before the isin filter in applied. When I do this I get the error

'SeriesGroupBy' object has no attribute 'isin'

Example explaining what I'm trying to do:

 import pandas as pd

dict = {'AttributeName': {0: 'John', 1: 'John', 2: 'John', 3: 'John', 4: 'Sally', 5: 'Sally'}, 'Lineage Step': {0: 1, 1: 2, 2: 3, 3: 4, 4:1, 5:2}, 'From Country': {0: 'Spain', 1: 'Scotland', 2: 'England', 3: 'England', 4: 'Scotland', 5:'England'}, 'From Town': {0: 'Madrid', 1: 'Edinburgh', 2: 'London', 3: 'London', 4: 'Edinburgh', 5: 'Manchester'}, 'FromStreet': {0: 'Spanish St', 1: 'Main St', 2: 'Lower St', 3: 'Middle St', 4: 'London St', 5: 'Scotland St'}, 'ToCountry': {0: 'Scotland', 1: 'England', 2: 'England', 3: 'England', 4: 'England', 5: 'England'}, 'ToTown': {0: 'Edinburgh', 1: 'London', 2: 'London', 3: 'London', 4: 'Liverpool', 5: 'London'}, 'ToStreet': {0: 'Lower St', 1: 'Middle St', 2: 'Upper St', 3: 'Upper St', 4: 'new St', 5: 'Old St'}}
sample_data = pd.DataFrame.from_dict(dict)

#example data set. I want to find every unique 'fromCountry' for both John and Sally. So For John we would just have the first row, where he enters from Spain to Scotland. The second row would be filtered as Scotland appears in the 'ToCountry' column. Sally would just have the 'FromCountry' Edinburgh row. 

I have tried to do like this:

sample_grouped = sample_data.groupby('AttributeName')
sample_grouped[~sample_grouped['From Country'].isin(sample_grouped['ToCountry'])]

but I get there error 'SeriesGroupBy' object has no attribute 'isin'

Does anyone know how to use the isin (or comparable) function on grouped by data?

Thanks

Hello everyone!
I am a newbie at python and I looked up some problems associated with the Data Expo 2009: Airline on time data from the Harvard Dataverse (https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/HG7NV7).
I am currently working on the following question:

1. When is the best time of day, day of the week, and time of year to fly to minimize delays?

All libraries are imported and the data is cleared up (empty columns and duplicate rows are dropped).
What I was intending to do is to plot a bar chart with "Months" on the x-axis and "ArrDelay" (arrival delays) on the y-axis.

My code looks the following way (I'm using jupyter notebook):

import pandas as pd 
dataair = pd.read_csv("/Users/issakovakamilla/Desktop/2000.csv.bz2")
dataair.dropna(how='all', axis=1, inplace=True)
dataair
import matplotlib.pyplot as plt
df = pd.DataFrame(dataair)
X = list(df.iloc[:, 0])
Y = list(df.iloc[:, 1])
plt.bar(X, Y, color='g')
plt.title("stats")
plt.xlabel("Month")
plt.ylabel("ArrDelay")
plt.show()

Somehow I don't get a plot - its been executing for 10 minutes now (I get * near input). Could anyone help me with this?

Hello,

Has anyone ever come across this before?

I'm trying to group some data in a dataframe and getting this error. The steps I've taken are:

1. in a for loop:

read in a csv from an api using pd.read_csv() replaced some values in a column using a for loop and .loc[] appended the resulting data frame to a list

2) concatenated the list of dataframes using pd.concat()

3) added a calculated column to the new DF by multiplying another column

4) added two empty columns

5) filtered the DF using .loc[] based on a value within a column

6) filtered the DF using .loc[] based on a value in a different column

7) tried to use this code:

new_DF = old_df.group_by(['col1', 'col_2', 'col_3', 'adgroup', 'col_4', 'col5', 'col6'], as_index=False)[['col7', 'col8', 
'col9']].sum()

The DF seems to behaving normally for example I can do dtypes and columns on it and add columns which are calculated from other columns. What is super frustrating is that I can do pd.to_csv() and then pd.read_csv() on the DF and then I'm able to do the grouping I want (however this isn't ideal which is why I'm posting).

Any advice would be appreciated.

Cheers