I have a dataframe df:
df = pd.DataFrame({})
df['X'] = [3,8,11,6,7,8]
df['name'] = [1,1,1,2,2,2]
X name
0 3 1
1 8 1
2 11 1
3 6 2
4 7 2
5 8 2
For each group within 'name' and want to remove that group if the difference between the first and last row of that group is smaller than a specified value d_dif in absolute way:
For example, when d_dif= 5, I want to get:
X name
0 3 1
1 8 1
2 11 1
If your data is increasingly in X, you can use groupby().transform() and np.ptp
threshold = 5
ranges = df.groupby('name')['X'].transform(np.ptp)
df[ranges > threshold]
If you only care about first and last, then transform just first and last:
threshold = 5
groups = df.groupby('name')['X']
ranges = groups.transform('last') - groups.transform('first')
df[ranges.abs() > threshold]
Related
The problem consist on calculate from a dataframe the column "accumulated" using the columns "accumulated" and "weekly". The formula to do this is accumulated in t = weekly in t + accumulated in t-1
The desired result should be:
weekly accumulated
2 0
1 1
4 5
2 7
The result I'm obtaining is:
weekly accumulated
2 0
1 1
4 4
2 2
What I have tried is:
for key, value in df_dic.items():
df_aux = df_dic[key]
df_aux['accumulated'] = 0
df_aux['accumulated'] = (df_aux.weekly + df_aux.accumulated.shift(1))
#df_aux["accumulated"] = df_aux.iloc[:,2] + df_aux.iloc[:,3].shift(1)
df_aux.iloc[0,3] = 0 #I put this because I want to force the first cell to be 0.
Being df_aux.iloc[0,3] the first row of the column "accumulated".
What I´m doing wrong?
Thank you
EDIT: df_dic is a dictionary with 5 dataframes. df_dic is seen as {0: df1, 1:df2, 2:df3}. All the dataframes have the same size and same columns names. So i do the for loop to do the same calculation in every dataframe inside the dictionary.
EDIT2 : I'm trying doing the computation outside the for loop and is not working.
What im doing is:
df_auxp = df_dic[0]
df_auxp['accumulated'] = 0
df_auxp['accumulated'] = df_auxp["weekly"] + df_auxp["accumulated"].shift(1)
df_auxp.iloc[0,3] = df_auxp.iloc[0,3].fillna(0)
Maybe have something to do with the dictionary interaction...
To solve for 3 dataframes
import pandas as pd
df1 = pd.DataFrame({'weekly':[2,1,4,2]})
df2 = pd.DataFrame({'weekly':[3,2,5,3]})
df3 = pd.DataFrame({'weekly':[4,3,6,4]})
print (df1)
print (df2)
print (df3)
for d in [df1,df2,df3]:
d['accumulated'] = d['weekly'].cumsum() - d.iloc[0,0]
print (d)
The output of this will be as follows:
Original dataframes:
df1
weekly
0 2
1 1
2 4
3 2
df2
weekly
0 3
1 2
2 5
3 3
df3
weekly
0 4
1 3
2 6
3 4
Updated dataframes:
df1:
weekly accumulated
0 2 0
1 1 1
2 4 5
3 2 7
df2:
weekly accumulated
0 3 0
1 2 2
2 5 7
3 3 10
df3:
weekly accumulated
0 4 0
1 3 3
2 6 9
3 4 13
To solve for 1 dataframe
You need to use cumsum and then subtract the value from first row. That will give you the desired result. here's how to do it.
import pandas as pd
df = pd.DataFrame({'weekly':[2,1,4,2]})
print (df)
df['accumulated'] = df['weekly'].cumsum() - df.iloc[0,0]
print (df)
Original dataframe:
weekly
0 2
1 1
2 4
3 2
Updated dataframe:
weekly accumulated
0 2 0
1 1 1
2 4 5
3 2 7
Suppose I have dataframe like this
>>> df = pd.DataFrame({'id':[1,1,1,2,2,2,2,3,4],'value':[1,2,3,1,2,3,4,1,1]})
>>> df
id value
0 1 1
1 1 2
2 1 3
3 2 1
4 2 2
5 2 3
6 2 4
7 3 1
8 4 1
Now I want top all records from each group using group id except last 3. That means I want to drop last 3 records from all groups. How can I do it using pandas group_by. This is dummy data.
Use GroupBy.cumcount for counter from back by ascending=False and then compare by Series.gt for greater values like 2, because python count from 0:
df = df[df.groupby('id').cumcount(ascending=False).gt(2)]
print (df)
id value
3 2 1
Details:
print (df.groupby('id').cumcount(ascending=False))
0 2
1 1
2 0
3 3
4 2
5 1
6 0
7 0
8 0
dtype: int64
I want to extract part of the data frame when value change from 0 to 1.
logic1: when value change from 0 to 1, start to save data until value again change to 0. (also points before 1 and after 1)
logic2: when value change from 0 to 1, start to save data until value again change to 0. (don't need to save points before 1 and after 1)
only save data when the first time value of flag change from 0 to 1, after this if again value change from 0 to 1 don't need to do anything
df=pd.DataFrame({'value':[3,4,7,8,11,1,15,20,15,16,87],'flag':[0,0,0,1,1,1,0,0,1,1,0]})
Desired output:
df_out_1=pd.DataFrame({'value':[7,8,11,1,15]})
Desired output:
df_out_2=pd.DataFrame({'value':[8,11,1]})
Idea is get consecutive groups of 1 and 0 consecutive groups to s, filter only 1 groups and get first 1 group by compare by minimal value:
df = df.reset_index(drop=True)
s = df['flag'].ne(df['flag'].shift()).cumsum()
m = s.eq(s[df['flag'].eq(1)].min())
df2 = df.loc[m, ['value']]
print (df2)
value
3 8
4 11
5 1
And then filter values with aff and remove 1 to default RangeIndex:
df1 = df.loc[(df2.index + 1).union(df2.index - 1), ['value']]
print (df1)
value
2 7
3 8
4 11
5 1
6 15
I have a pandas dataframe like below:
data=[['A',1,30],
['A',1,2],
['A',0,4],
['A',1,4],
['B',0,5],
['B',1,1],
['B',0,5],
['B',1,8]]
df = pd.DataFrame(data,columns=['group','var_1','var_2'])
I want to create a series of values with index based on below condition:
Step 1) Increment should always happen from 1st row of 'var_2'of each group. For example: for group A, the increment should start from 30 and for group B,
increment should start from 5
Step 2) Incremented value where 'var_1" = 1
My desired output:
0 30
1 31
3 32
5 6
7 7
IIUC:
#Get first index in each group and union index where var_1 ==1
indx = df.drop_duplicates('group').index.union(df[(df['var_1']==1)].index)
#Reindex dataframe group by group, add cusum value to other present values in group.
#Use .loc to filter where var_1 != 0 and get column var_2
df.reindex(indx).groupby('group')\
.transform(lambda x: x.iloc[0] + x.shift().notna().cumsum())\
.loc[lambda x: x.var_1 !=0, 'var_2']
Output:
0 30
1 31
3 32
5 6
7 7
Name: var_2, dtype: int64
Try groupby cumcount and first
df1 = df.loc[df.var_1.eq(1)]
g = df1.groupby('group')['var_2']
g.transform('first') + g.cumcount()
Out[66]:
0 30
1 31
3 32
5 1
7 2
dtype: int64
Or use duplicated with df.where and cumsum
df1 = df.loc[df.var_1.eq(1)]
df1.var_2.where(~df1.duplicated('group'), 1).groupby(df1.group).cumsum()
Out[77]:
0 30
1 31
3 32
5 1
7 2
Name: var_2, dtype: int64
in my dataframe have three columns columns value ,ID and distance . i want to check in ID column when its changes from 2 to any other value count rows and record first value and last value when 2 changes to other value and save and also save corresponding value of column distance when change from 2 to other in ID column.
df=pd.DataFrame({'value':[3,4,7,8,11,20,15,20,15,16],'ID':[2,2,8,8,8,2,2,2,5,5],'distance':[0,0,1,0,0,0,0,0,0,0]})
print(df)
value ID distance
0 3 2 0
1 4 2 0
2 7 8 1
3 8 8 0
4 11 8 0
5 20 2 0
6 15 2 0
7 20 2 0
8 15 5 0
9 16 5 0
required results:
df_out=pd.DataFrame({'rows_Count':[3,2],'value_first':[7,15],'value_last':[11,16],'distance_first':[1,0]})
print(df_out)
rows_Count value_first value_last distance_first
0 3 7 11 1
1 2 15 16 0
Use:
#compare by 2
m = df['ID'].eq(2)
#filter out data before first 2 (in sample data not, in real data possible)
df = df[m.cumsum().ne(0)]
#create unique groups for non 2 groups, add misisng values by reindex
s = m.ne(m.shift()).cumsum()[~m].reindex(df.index)
#aggregate with helper s Series
df1 = df.groupby(s).agg({'ID':'size', 'value':['first','last'], 'distance':'first'})
#flatten MultiIndex
df1.columns = df1.columns.map('_'.join)
df1 = df1.reset_index(drop=True)
print (df1)
ID_size value_first value_last distance_first
0 3 7 11 1
1 2 15 16 0
Verify in changed data (not only 2 first group):
df=pd.DataFrame({'value':[3,4,7,8,11,20,15,20,15,16],
'ID':[1,7,8,8,8,2,2,2,5,5],
'distance':[0,0,1,0,0,0,0,0,0,0]})
print(df)
value ID distance
0 3 1 0 <- changed ID
1 4 7 0 <- changed ID
2 7 8 1
3 8 8 0
4 11 8 0
5 20 2 0
6 15 2 0
7 20 2 0
8 15 5 0
9 16 5 0
#compare by 2
m = df['ID'].eq(2)
#filter out data before first 2 (in sample data not, in real data possible)
df = df[m.cumsum().ne(0)]
#create unique groups for non 2 groups, add misisng values by reindex
s = m.ne(m.shift()).cumsum()[~m].reindex(df.index)
#aggregate with helper s Series
df1 = df.groupby(s).agg({'ID':'size', 'value':['first','last'], 'distance':'first'})
#flatten MultiIndex
df1.columns = df1.columns.map('_'.join)
df1 = df1.reset_index(drop=True)
print (df1)
ID_size value_first value_last distance_first
0 2 15 16 0