This question already has an answer here:
How is pandas groupby method actually working?
(1 answer)
Closed 3 years ago.
I want to use groupby function in pandas. However it doesn't work. Other function in pandas works very well. I'm very confused.
corr=corr.groupby(level='date')
print(corr)
Out[21]: <pandas.core.groupby.generic.DataFrameGroupBy object at 0x0000025FC4726808> ``
You need to apply an aggregation to it, for example
corr.groupby(level='date').sum()
Related
This question already has answers here:
Pandas reset index is not taking effect [duplicate]
(4 answers)
Closed 3 months ago.
Please see images -
After creating a dataframe, I use groupby, then I reset the index column only to find that the column for 'county' is still unseen by the dataframe. Please help to rectify.
The df.reset_index() by default is not an "inplace" operation. But with use of the inplace parameter you can make it behave as such.
1. Either use inplace=True -
mydf.reset_index(inplace=True)
2. Or save the df into another (or the same) variable -
mydf = mydf.reset_index()
This should fix your issue.
This question already has an answer here:
Pandas, loc vs non loc for boolean indexing
(1 answer)
Closed 2 years ago.
I am learning pandas and want to know the best practice for filtering rows of a DataFrame by column values.
According to https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html, the recommendation is to use optimized pandas data access methods such as .loc
An example from https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.loc.html -
df.loc[df['shield'] > 6]
However, according to https://pandas.pydata.org/docs/getting_started/comparison/comparison_with_sql.html#where, a construction like tips[tips['time'] == 'Dinner'] could be used.
Why is the recommended .loc omitted? Is there any difference?
With .loc you can also correctly set a value, as not using it raises an you are trying to set a value on a copy of a DataFrame error. For getting something out of your DataFrame, there might be performance differences, but I don't know that.
This question already has an answer here:
pandas groupby two columns and summarize by mean
(1 answer)
Closed 3 years ago.
I've the pandas dataframe like below
.
I want to transform this DataFrame into another form like below.
I've tried groupby functionality in pandas.But could not able to achieve the solution. Please help me with suggestions. Thanks inadvance.
e=df.groupby(['Country','City'])['Rating'].mean()
pd.DataFrame(e)
This would look like
This question already has answers here:
drop_duplicates not working in pandas?
(7 answers)
DataFrame.drop_duplicates and DataFrame.drop not removing rows
(2 answers)
Closed 3 years ago.
I've got a DataSet with two columns, one with categorical value (State2), and another (State) that contains the same values only in binary.
I used OneHotEncoding.
import pandas as pd
mydataset = pd.read_csv('fieldprotobackup.binetflow')
mydataset.drop_duplicates(['Proto2','Proto'], keep='first')
mydataset.to_csv('fieldprotobackup.binetflow', columns=['Proto2','Proto'], index=False)
Dataset
I'd like to remove all redundancies from the file. While researching, I found the command df.drop_duplicates, but it's not working for me.
You either need to add the inplace=True parameter, or you need to capture the returned dataframe:
mydataset.drop_duplicates(['Proto2','Proto'], keep='first', inplace=True)
or
no_duplicates = mydataset.drop_duplicates(['Proto2','Proto'], keep='first')
Always a good idea to check the documentation when something isn't working as expected.
This question already has answers here:
Action with pandas SettingWithCopyWarning
(1 answer)
Confusion re: pandas copy of slice of dataframe warning
(1 answer)
Pandas: SettingWithCopyWarning, trying to understand how to write the code better, not just whether to ignore the warning
(2 answers)
Closed 5 years ago.
My attempt: df['uid'] = df.uid.astype(int)
Which works...! However, Python doesn't like it:
A value is trying to be set on a copy of a slice from a DataFrame. Try
using .loc[row_indexer,col_indexer] = value instead
My question - what's the "best practice" of how to do this simple code?
Research so far:
Pandas: change data type of columns
A value is trying to be set on a copy of a slice from a DataFrame Warning
Attempts:
df[df['uid']].astype(int)
...some of the links say to use iloc but I can't see how that can be used here...