I have a data frame that looks like below:
import pandas as pd
df = pd.DataFrame({'Date':[2019-08-06,2019-08-08,2019-08-01,2019-10-12], 'Name':['A','A','B','C'], 'grade':[100,90,69,80]})
I want to groupby the data by month and year from the Datetime and also group by Name. Then sum up the other columns.
So, the desired output will be something similar to this
df = pd.DataFrame({'Date':[2019-08, 2019-08, 2019-10-12], 'Name':['A','B','C'], 'grade':[190,69,80]})
I have tried grouper
df.groupby(pd.Grouper(freq='M').sum()
However, it won't take the Name column into play and just drop the entire column.
Try :
df['Date'] = pd.to_datetime(df.Date)
df.groupby([df.Date.dt.to_period('M'), 'Name']).sum().reset_index()
Date Name grade
0 2019-08 A 190
1 2019-08 B 69
2 2019-10 C 80
I assume date column is of dtype datetime. Then group with
grouped = df.groupby([df.Date.dt.year, df.Date.dt.month, 'Name']).sum()
Related
I have data frame that I want to groupby by two columns one of them is datetime type. How can I do this?
import pandas as pd
import datetime as dt
df = pd.DataFrame({
'a':np.random.randn(6),
'b':np.random.choice( [5,7,np.nan], 6),
'g':{1002,300,1002,300,1002,300}
'c':np.random.choice( ['panda','python','shark'], 6),
# some ways to create systematic groups for indexing or groupby
# this is similar to r's expand.grid(), see note 2 below
'd':np.repeat( range(3), 2 ),
'e':np.tile( range(2), 3 ),
# a date range and set of random dates
'f':pd.date_range('1/1/2011', periods=6, freq='D'),
'g':np.random.choice( pd.date_range('1/1/2011', periods=365,
freq='D'), 6, replace=False)
})
You can use pd.Grouper to specify groupby instructions. It can be used with pd.DatetimeIndex index to group data with specified frequency using the freq parameter.
Assumming that you have this dataframe:
df = pd.DataFrame(dict(
a=dict(date=pd.Timestamp('2020-05-01'), category='a', value=1),
b=dict(date=pd.Timestamp('2020-06-01'), category='a', value=2),
c=dict(date=pd.Timestamp('2020-06-01'), category='b', value=6),
d=dict(date=pd.Timestamp('2020-07-01'), category='a', value=1),
e=dict(date=pd.Timestamp('2020-07-27'), category='a', value=3),
)).T
You can set index to date column and it would be converted to pd.DatetimeIndex. Then you can use pd.Grouper among with another columns. For the following example I use category column.
freq='M' parameter used to group index using month frequency. There are number of string data series aliases that can be used in pd.Grouper
df.set_index('date').groupby([pd.Grouper(freq='M'), 'category'])['value'].sum()
Result:
date category
2020-05-31 a 1
2020-06-30 a 2
b 6
2020-07-31 a 4
Name: value, dtype: int64
Another example with your mcve:
df.set_index('g').groupby([pd.Grouper(freq='M'), 'c']).d.sum()
Result:
g c
2011-01-31 panda 0
2011-04-30 shark 2
2011-06-30 panda 2
2011-07-31 panda 0
2011-09-30 panda 1
2011-12-31 python 1
Name: d, dtype: int32
Year A B C D
1900 1 2 3 4
1901 2 3 4 5
I have a dataset which aligns with the above format.
When i want to perform calculations on column values the year is getting added to the column values and distorting the result. For example
df['mean'] = df.mean(axis='columns')
In the above example i just want to exclude year from calculations. I have 100 plus columns in my data frame and i cannot manually use each of the columns . 'year' is also the Index for my dataframe
I realized the problem and solution
df.set_index(['Year']
df['mean'] = df.mean(axis='columns')
This did not work
But when i added inplace = True , it worked.
'df.set_index(['Year'],inplace = True)'
df['mean'] = df.mean(axis='columns')
You can also drop the year column and create a new dataframe and after applying the mean to individual columns we can add the year column.
df2 = df.drop('Year')
df2['Mean']=df.mean(axis='columns')
df2.concat(df.Year,df2)
my df:
df=pd.DataFrame({'A':['Adam','Adam','Adam','Adam'],'B':[24,90,67,12]})
I want to select only rows with same name with min and max value in this df.
i can do that using this code:
df_max=df[df['B']==(df.groupby(['A'])['B'].transform(max))]
df_min=df[df['B']==(df.groupby(['A'])['B'].transform(min))]
df=pd.concat([df_max,df_min])
Is there any way to do this in one line? i prefer to not create two additional df's and concat them at the end .
Thanks
Use GroupBy.agg with DataFrameGroupBy.idxmax and DataFrameGroupBy.idxmin with reshape by DataFrame.melt and select rows by DataFrame.loc:
df1 = df.loc[df.groupby('A')['B'].agg(['idxmax','idxmin']).melt()['value']].drop_duplicates()
Or DataFrame.stack:
df2 = df.loc[df.groupby('A')['B'].agg(['idxmax','idxmin']).stack()].drop_duplicates()
print (df2)
A B
1 Adam 90
3 Adam 12
A solution using groupby, apply and loc to select only the min or max value of column 'B'.
ddf = df.groupby('A').apply(lambda x : x.loc[(x['B'] == x['B'].min()) | (x['B'] == x['B'].max())]).reset_index(drop=True)
The result is:
A B
0 Adam 90
1 Adam 12
I got a DataFrame with these columns :
year month day gender births
I'd like to create a new column type "Date" based on the column year, month and day as : "yyyy-mm-dd"
I'm just beginning in Python and I just can't figure out how to proceed...
Assuming you are using pandas to create your dataframe, you can try:
>>> import pandas as pd
>>> df = pd.DataFrame({'year':[2015,2016],'month':[2,3],'day':[4,5],'gender':['m','f'],'births':[0,2]})
>>> df['dates'] = pd.to_datetime(df.iloc[:,0:3])
>>> df
year month day gender births dates
0 2015 2 4 m 0 2015-02-04
1 2016 3 5 f 2 2016-03-05
Taken from the example here and the slicing (iloc use) "Selection" section of "10 minutes to pandas" here.
You can use .assign
For example:
df2= df.assign(ColumnDate = df.Column1.astype(str) + '- ' + df.Column2.astype(str) + '-' df.Column3.astype(str) )
It is simple and it is much faster than lambda if you have tonnes of data.
I have the following test dataframe:
date user answer
0 2018-08-19 19:08:19 pga yes
1 2018-08-19 19:09:27 pga no
2 2018-08-19 19:10:45 lry no
3 2018-09-07 19:12:31 lry yes
4 2018-09-19 19:13:07 pga yes
5 2018-10-22 19:13:20 lry no
I am using the following code to group by week:
test.groupby(pd.Grouper(freq='W'))
I'm getting an error that Grouper is only valid with DatetimeIndex, however I'm unfamiliar on how to structure this in order to group by week.
Probably you have date column as a string.
In order to use it in a Grouper with a frequency, start from converting this column to DateTime:
df['date'] = pd.to_datetime(df['date'])
Then, as date column is an "ordinary" data column (not the index), use key='date' parameter and a frequency.
To sum up, below you have a working example:
import pandas as pd
d = [['2018-08-19 19:08:19', 'pga', 'yes'],
['2018-08-19 19:09:27', 'pga', 'no'],
['2018-08-19 19:10:45', 'lry', 'no'],
['2018-09-07 19:12:31', 'lry', 'yes'],
['2018-09-19 19:13:07', 'pga', 'yes'],
['2018-10-22 19:13:20', 'lry', 'no']]
df = pd.DataFrame(data=d, columns=['date', 'user', 'answer'])
df['date'] = pd.to_datetime(df['date'])
gr = df.groupby(pd.Grouper(key='date',freq='W'))
for name, group in gr:
print(' ', name)
if len(group) > 0:
print(group)
Note that the group key (name) is the ending date of a week, so dates from group members are earlier or equal to the date printed above.
You can change it passing label='left' parameter to Grouper.