I want to update the first row of a dataframe that meets a certain condition. Like in this question Get first row of dataframe in Python Pandas based on criteria but for setting instead of just selecting.
df[df['Qty'] > 0].iloc[0] = 5
The above line does not seem to do anything.
Given df below:
a b
0 1 2
1 2 1
2 3 1
you change the values in the first row where the value in column b is equal to 1 by:
df.loc[df[df['b'] == 1].index[0]] = 1000
Output:
a b
0 1 2
1 1000 1000
2 3 1
If you want to change the value in a specific column(s), you can do that too:
df.loc[df[df['b'] == 1].index[0],'a'] = 1000
a b
0 1 2
1 1000 1
2 3 1
I believe what you're looking for is:
idx = df[df['Qty'] > 0].index[0]
df.loc[[idx], ['Qty']] = 5
Related
I am having trouble with Pandas.
I try to compare each value of a row to another one.
In the attached link you will be able to see a slice of my dataframe.
For each date I have the daily variation of some stocks.
I want to compare each stock variation to the variation of the columns labelled 'CAC 40'.
If the value is greater I want to turn it into a Boolean 1 or 0 if lower.
This should return a dataframe filled only with 1 or 0 so I can then summarize by columns.
I have tried the apply method but this doesn't work.
It returns a Pandas.Serie ( attached below )
def compare_to_cac(row):
for i in row:
if row[i] >= row['CAC 40']:
return 1
else:
return 0
data2 = data.apply(compare_to_cac, axis=1)
Please can someone help me out ?
I worked with this data (column names are not important here, only the CAC 40 one is):
A B CAC 40
0 0 2 9
1 1 3 9
2 2 4 1
3 3 5 2
4 4 7 2
With just a for loop :
for column in df.columns:
if column == "CAC 40":
continue
condition = [df[column] > df["CAC 40"]]
value = [1]
df[column] = np.select(condition, value, default=0)
Which gives me as a result :
A B CAC 40
0 0 0 9
1 0 0 9
2 1 1 1
3 1 1 2
4 1 1 2
sample and expected data
The block one is current data and block 2 is the expected data that is, when i encounter 1 i need the next row to be incremented by one and for next country b same should happen
First replace all another values after first 1 to 1, so is possible use GroupBy.cumsum:
df = pd.DataFrame({'c':['a']*3 + ['b']*3+ ['c']*3, 'v':[1,0,0,0,1,0,0,0,1]})
s = df.groupby('c')['v'].cumsum()
df['new'] = s.where(s.eq(0), 1).groupby(df['c']).cumsum()
print (df)
c v new
0 a 1 1
1 a 0 2
2 a 0 3
3 b 0 0
4 b 1 1
5 b 0 2
6 c 0 0
7 c 0 0
8 c 1 1
Another solution is replace all not 1 values to missing values and forward filling 1 per groups, then first missing values are replaced to 0, so cumulative sum also working perfectly:
s = df['v'].where(df['v'].eq(1)).groupby(df['c']).ffill().fillna(0).astype(int)
df['new'] = s.groupby(df['c']).cumsum()
I have a dataset as below:
import pandas as pd
dict = {"A":[1,1,1,1,5],"B":[1,1,2,4,1]}
dt = pd.DataFrame(data=dict)
so, it is as below:
A B
1 1
1 1
1 2
1 4
5 1
i need to apply a groupby based on A and B count how many records each group has?
i have applied the below solution:
dtSize = dt.groupby(by=["A","B"], as_index=False).size()
dtSize.to_csv("./datasets/Final DT/dtSize.csv", sep=',', encoding='utf-8', index=False)
I have 2 problems:
When i open the saved file, it only contains the last column which includes number element in each group, but it does not include the groups
when i print the final dtSize it is as below:
so, some similar records in A is missed.
My favorit output is as below in a .csv file
A B Number of elements in group
1 1 2
1 2 1
1 4 1
5 1 1
Actually, data from A isn't missing. GroupBy.size returns a Series, so A and B are used as a MultiIndex. Due to this, repeated values for A in the first three rows aren't printed.
You're close. You need to reset the index and, optionally, name the result:
dt.groupby(['A', 'B']).size().reset_index(name='Size')
The result is:
A B Size
0 1 1 2
1 1 2 1
2 1 4 1
3 5 1 1
My dataframe looks like this:
Country Code Duration
A 1 0
A 1 1
A 1 2
A 1 3
A 2 0
A 2 1
A 1 0
A 1 1
A 1 2
I need to get max values from a "Duration" column - not just a maximum value, but a list of maximum values for each sequence of numbers in this column. The output might look like this:
Country Code Duration
A 1 3
A 2 1
A 1 2
I could have grouped by "Code", but its values are often repeating, so that's probably not an option. Any help or tips would be much appreciated.
Using idxmax after create another group key by diff and cumsum
df.loc[df.groupby([df.Country,df.Code.diff().ne(0).cumsum()]).Duration.idxmax()]
Country Code Duration
3 A 1 3
5 A 2 1
8 A 1 2
First we create a mask to mark the sequences. Then we groupby to create the wanted output:
m = (~df['Code'].eq(df['Code'].shift())).cumsum()
df.groupby(m).agg({'Country':'first',
'Code':'first',
'Duration':'max'}).reset_index(drop=True)
Country Code Duration
0 A 1 3
1 A 2 1
2 A 1 2
The problem is slightly unclear. However, assuming that order is important, we can move toward a solution.
import pandas as pd
d = pd.read_csv('data.csv')
s = d.Code
d['series'] = s.ne(s.shift()).cumsum()
print(pd.DataFrame(d.groupby(['Country','Code','series'])['Duration'].max().reset_index()))
Returns:
Country Code series Duration
0 A 1 1 3
1 A 1 3 2
2 A 2 2 1
You can then drop the series.
You might wanna check this link , it might be the answer you're looking for :
pandas groupby where you get the max of one column and the min of another column . It goes as :
result = df.groupby(['Code', 'Country']).agg({'Duration':'max'})[['Duration']].reset_index()
My system
Windows 7, 64 bit
python 3.5.1
The challenge
I've got a pandas dataframe, and I would like to know the maximum value for each row, and append that info as a new column. I would also like to know the name of the column where the maximum value is located. And I would like to add another column to the existing dataframe containing the name of the column where the max value can be found.
A similar question has been asked and answered for R in this post.
Reproducible example
In[1]:
# Make pandas dataframe
df = pd.DataFrame({'a':[1,0,0,1,3], 'b':[0,0,1,0,1], 'c':[0,0,0,0,0]})
# Calculate max
my_series = df.max(numeric_only=True, axis = 1)
my_series.name = "maxval"
# Include maxval in df
df = df.join(my_series)
df
Out[1]:
a b c maxval
0 1 0 0 1
1 0 0 0 0
2 0 1 0 1
3 1 0 0 1
4 3 1 0 3
So far so good. Now for the add another column to the existing dataframe containing the name of the column part:
In[2]:
?
?
?
# This is what I'd like to accomplish:
Out[2]:
a b c maxval maxcol
0 1 0 0 1 a
1 0 0 0 0 a,b,c
2 0 1 0 1 b
3 1 0 0 1 a
4 3 1 0 3 a
Notice that I'd like to return all column names if multiple columns contain the same maximum value. Also please notice that the column maxval is not included in maxcol since that would not make much sense. Thanks in advance if anyone out there finds this interesting.
You can compare the df against maxval using eq with axis=0, then use apply with a lambda to produce a boolean mask to mask the columns and join them:
In [183]:
df['maxcol'] = df.ix[:,:'c'].eq(df['maxval'], axis=0).apply(lambda x: ','.join(df.columns[:3][x==x.max()]),axis=1)
df
Out[183]:
a b c maxval maxcol
0 1 0 0 1 a
1 0 0 0 0 a,b,c
2 0 1 0 1 b
3 1 0 0 1 a
4 3 1 0 3 a