I am new to pandas and I am facing the following problem:
I have 2 data frames:
df1 :
x y
1 3 4
2 nan
3 6
4 nan
5 9 2
6 1 4 9
df2:
x y
1 2 3 6 1 5
2 4 1 8 7 5
3 6 3 1 4 5
4 2 1 3 5 4
5 9 2 3 8 7
6 1 4 5 3 7
The size of the two is same.
I want to merge the two dataframes such that all the resulting dataframe i get is the following:
result :
x y
1 3 4 6 1 5
2 4 1 8 7 5
3 6 3 1 4 5
4 2 1 3 5 4
5 9 2 3 8 7
6 1 4 5 6 7
So in the result, priority is given to df2. If there is a value in df2, it is put first and the remaining values are put from df1 (they have the same position as in df1). There should be no repeated values in the result (i.e if a value is in position 1 in df1 and position 3 in df2, then that value should come only in position 1 in the result and not repeat)
Any kind of help will be appreciated.
Thanks!
IIUC
Setup
df1 = pd.DataFrame(dict(x=range(1, 7),
y=[[3, 4], None, [6], None, [9, 2], [1, 4, 9]]))
df2 = pd.DataFrame(dict(x=range(1, 7), y=[[2, 3, 6, 1, 5], [4, 1, 8, 7, 5],
[6, 3, 1, 4, 5], [2, 1, 3, 5, 4],
[9, 2, 3, 8, 7], [1, 4, 5, 3, 7]]))
print df1
print
print df2
x y
0 1 [3, 4]
1 2 None
2 3 [6]
3 4 None
4 5 [9, 2]
5 6 [1, 4, 9]
x y
0 1 [2, 3, 6, 1, 5]
1 2 [4, 1, 8, 7, 5]
2 3 [6, 3, 1, 4, 5]
3 4 [2, 1, 3, 5, 4]
4 5 [9, 2, 3, 8, 7]
5 6 [1, 4, 5, 3, 7]
convert to something more usable:
df1_ = df1.set_index('x').y.apply(pd.Series)
df2_ = df2.set_index('x').y.apply(pd.Series)
print df1_
print
print df2_
0 1 2
x
1 3.0 4.0 NaN
2 NaN NaN NaN
3 6.0 NaN NaN
4 NaN NaN NaN
5 9.0 2.0 NaN
6 1.0 4.0 9.0
0 1 2 3 4
x
1 2 3 6 1 5
2 4 1 8 7 5
3 6 3 1 4 5
4 2 1 3 5 4
5 9 2 3 8 7
6 1 4 5 3 7
Combine with priority given to df1 (I think you meant df1 as that what was consistent with my interpretation of your question and the expected output you provided) then reducing to eliminate duplicates:
print df1_.combine_first(df2_).apply(lambda x: x.unique(), axis=1)
0 1 2 3 4
x
1 3 4 6 1 5
2 4 1 8 7 5
3 6 3 1 4 5
4 2 1 3 5 4
5 9 2 3 8 7
6 1 4 9 3 7
Related
example df:
df2 = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9],[1, 2, 3], [4, 5, 6], [7, 8, 9],[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
columns=['a', 'b', 'c'])
a b c
0 1 2 3
1 4 5 6
2 7 8 9
3 1 2 3
4 4 5 6
5 7 8 9
6 1 2 3
7 4 5 6
8 7 8 9
Goal is to get a new column, 'd', that returns True when a certain condition is true anywhere within a rolling window of size n.
For example, desired column 'd' for condition "column c == 2 within rolling window of 2":
a b c d
0 1 2 3 nan
1 4 5 6 True
2 7 8 9 False
3 1 2 3 True
4 4 5 6 True
5 7 8 9 False
6 1 2 3 True
7 4 5 6 True
8 7 8 9 False
I hope my question is understood thank you for taking your time
To be clear, I am trying to return True if ANY of the rows in the rolling window return True
I imagine you meant column b:
s = df2['b'].eq(2).rolling(2).max()
df2['d'] = s.astype(bool).mask(s.isna())
NB. you need to use max as rolling only works with numeric data.
Output:
a b c d
0 1 2 3 NaN
1 4 5 6 True
2 7 8 9 False
3 1 2 3 True
4 4 5 6 True
5 7 8 9 False
6 1 2 3 True
7 4 5 6 True
8 7 8 9 False
I have a df
df = pd.DataFrame([
[1, 1, 'A', 10],
[4, 1 ,'A', 6],
[7, 2 ,'A', 3],
[2, 2 ,'A', 4],
[6, 2 ,'B', 9],
[5, 2 ,'B', 7],
[5, 1 ,'B', 12],
[5, 1 ,'B', 4],
[5, 2 ,'C', 9],
[5, 1 ,'C', 3],
[5, 1 ,'C', 4],
[5, 2 ,'C', 7]
],
index=['A', 'A', 'A','A','A','A','A','A','A','A','A','A'],
columns=['A', 'B', 'C', 'D'])
I can count the number of non zero values for column D grouped by column A using:
df['countTrans'] = df['D'].ne(0).groupby(df['A']).transform('sum')
where the output is:
df:
A B C D countTrans
A 1 1 A 10 1.0
A 4 1 A 0 0.0
A 7 2 A 3 1.0
A 2 2 A 4 1.0
A 6 2 B 9 1.0
A 5 2 B 7 7.0
A 5 1 B 12 7.0
A 5 1 B 4 7.0
A 5 2 C 9 7.0
A 5 1 C 3 7.0
A 5 1 C 4 7.0
A 5 2 C 7 7.0
however I would like to also group by not only by column A but also column B.
I have tried variants of:
df['countTrans'] = df['D'].ne(0).groupby(df['A'], df['B']).transform('sum')
df['countTrans'] = df['D'].ne(0).groupby(df['A','B']).transform('sum')
without success
my desired output would look like:
df:
A B C D countTrans
A 1 1 A 10 1.0
A 4 1 A 0 0.0
A 7 2 A 3 1.0
A 2 2 A 4 1.0
A 6 2 B 9 1.0
A 5 2 B 7 3.0
A 5 1 B 12 4.0
A 5 1 B 4 4.0
A 5 2 C 9 3.0
A 5 1 C 3 4.0
A 5 1 C 4 4.0
A 5 2 C 7 3.0
Possible solution is pass Series to list:
df['countTrans'] = df['D'].ne(0).groupby([df['A'], df['B']]).transform('sum')
print (df)
A B C D countTrans
A 1 1 A 10 1
A 4 1 A 6 1
A 7 2 A 3 1
A 2 2 A 4 1
A 6 2 B 9 1
A 5 2 B 7 3
A 5 1 B 12 4
A 5 1 B 4 4
A 5 2 C 9 3
A 5 1 C 3 4
A 5 1 C 4 4
A 5 2 C 7 3
Or create helper column by DataFrame.assign (more 'clean' in my opinion):
df['countTrans'] = df.assign(E = df['D'].ne(0)).groupby(['A','B'])['E'].transform('sum')
#similar solution with overwrite D
#df['countTrans'] = df.assign(D = df['D'].ne(0)).groupby(['A','B'])['D'].transform('sum')
I have a dataframe:
np.random.seed(1)
df1 = pd.DataFrame({'day':[3, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6],
'item': [1, 1, 2, 2, 1, 2, 3, 3, 4, 3, 4],
'price':np.random.randint(1,30,11)})
day item price
0 3 1 6
1 4 1 12
2 4 2 13
3 4 2 9
4 5 1 10
5 5 2 12
6 5 3 6
7 5 3 16
8 5 4 1
9 6 3 17
10 6 4 2
After the groupby code gb = df1.groupby(['day','item'])['price'].mean(), I get:
gb
day item
3 1 6
4 1 12
2 11
5 1 10
2 12
3 11
4 1
6 3 17
4 2
Name: price, dtype: int64
I want to get the trend from the groupby series replacing back into the dataframe column price. The price is the variation of the item-price with repect to the previous day price
day item price
0 3 1 nan
1 4 1 6
2 4 2 nan
3 4 2 nan
4 5 1 -2
5 5 2 1
6 5 3 nan
7 5 3 nan
8 5 4 nan
9 6 3 6
10 6 4 1
Please help me to code the last step. A single/double line code will be most helpful. As the actual dataframe is huge, I would like to avoid iterations.
Hope this helps!
#get the average values
mean_df=df1.groupby(['day','item'])['price'].mean().reset_index()
#rename columns
mean_df.columns=['day','item','average_price']
#sort by day an item in ascending
mean_df=mean_df.sort_values(by=['day','item'])
#shift the price for each item and each day
mean_df['shifted_average_price'] = mean_df.groupby(['item'])['average_price'].shift(1)
#combine with original df
df1=pd.merge(df1,mean_df,on=['day','item'])
#replace the price by difference of previous day's
df1['price']=df1['price']-df1['shifted_average_price']
#drop unwanted columns
df1.drop(['average_price', 'shifted_average_price'], axis=1, inplace=True)
I am trying to convert a list within multiple columns of a pandas DataFrame into separate columns.
Say, I have a dataframe like this:
0 1
0 [1, 2, 3] [4, 5, 6]
1 [1, 2, 3] [4, 5, 6]
2 [1, 2, 3] [4, 5, 6]
And would like to convert it to something like this:
0 1 2 0 1 2
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
I have managed to do this in a loop. However, I would like to do this in fewer lines.
My code snippet so far is as follows:
import pandas as pd
df = pd.DataFrame([[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]],[[1,2,3],[4,5,6]]])
output1 = df[0].apply(pd.Series)
output2 = df[1].apply(pd.Series)
output = pd.concat([output1, output2], axis=1)
If you don't care about the column names you could do:
>>> df.apply(np.hstack, axis=1).apply(pd.Series)
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
Using sum
pd.DataFrame(df.sum(1).tolist())
0 1 2 3 4 5
0 1 2 3 4 5 6
1 1 2 3 4 5 6
2 1 2 3 4 5 6
I am working with a pandas dataframe that something looks like this:
col1 col2 col3 col_num
0 [-0.20447069290738076, 0.4159556680196389, -0.... [-0.10935000772973974, -0.04425263358067333, -... [51.0834196, 10.4234469] 3160
1 [-0.42439951483476124, -0.3135960467759942, 0.... [0.3842614765721414, -0.06756644506033657, 0.4... [45.5643442, 17.0118954] 3159
3 [0.3158755226012898, -0.007057682056994253, 0.... [-0.33158941456615376, 0.09637640660002277, -0... [50.6402809, 4.6667145] 3157
5 [-0.011089723491692679, -0.01649481399305317, ... [-0.02827408211098023, 0.00019040943944721592,... [53.45733965, -2.22695880505223] 3157
I would like to concatenate vectors across rows as so:
df['col1'] + df['col2'] + df['col3'] + df['col_num'].transform(lambda item: [item])
However I am prompted with the following error:
/opt/conda/lib/python3.6/site-packages/pandas/core/ops.py in <lambda>(x)
708 if is_object_dtype(lvalues):
709 return libalgos.arrmap_object(lvalues,
--> 710 lambda x: op(x, rvalues))
711 raise
712
ValueError: operands could not be broadcast together with shapes (30,) (86597,)
It's looking like for some reason ti's getting stuck at concatenating the 3rd column, which only has 2 dimensions. The data is 86597 rows long. How can I fix this error?
You can convert problematic column to list like:
df['col1'] + df['col2'] + df['col3'].apply(list) + df['col_num'].transform(lambda x: [x])
Another solution is convert all lists to 2d numpy arrays and use hstack, if same length of lists in each column, because you lose the vectorised functionality which goes with using NumPy arrays held in contiguous memory blocks:
np.random.seed(123)
N = 10
df = pd.DataFrame({
"col1": [np.random.randint(10, size=3) for i in range(N)],
"col2": [np.random.randint(10, size=3) for i in range(N)],
"col3": [np.random.randint(10, size=2) for i in range(N)],
'col_num': range(N)
})
print (df)
col1 col2 col3 col_num
0 [2, 2, 6] [9, 3, 4] [2, 4] 0
1 [1, 3, 9] [6, 1, 5] [8, 1] 1
2 [6, 1, 0] [6, 2, 1] [2, 1] 2
3 [1, 9, 0] [8, 3, 5] [1, 3] 3
4 [0, 9, 3] [0, 2, 6] [5, 9] 4
5 [4, 0, 0] [2, 4, 4] [0, 8] 5
6 [4, 1, 7] [6, 3, 0] [1, 6] 6
7 [3, 2, 4] [6, 4, 7] [3, 3] 7
8 [7, 2, 4] [6, 7, 1] [5, 9] 8
9 [8, 0, 7] [5, 7, 9] [7, 9] 9
a = np.array(df['col1'].values.tolist())
b = np.array(df['col2'].values.tolist())
c = np.array(df['col3'].values.tolist())
#create Nx1 array
d = df['col_num'].values[:, None]
arr = np.hstack((a,b,c, d))
print (arr)
[[2 2 6 9 3 4 2 4 0]
[1 3 9 6 1 5 8 1 1]
[6 1 0 6 2 1 2 1 2]
[1 9 0 8 3 5 1 3 3]
[0 9 3 0 2 6 5 9 4]
[4 0 0 2 4 4 0 8 5]
[4 1 7 6 3 0 1 6 6]
[3 2 4 6 4 7 3 3 7]
[7 2 4 6 7 1 5 9 8]
[8 0 7 5 7 9 7 9 9]]
df = pd.DataFrame(arr)
print (df)
0 1 2 3 4 5 6 7 8
0 2 2 6 9 3 4 2 4 0
1 1 3 9 6 1 5 8 1 1
2 6 1 0 6 2 1 2 1 2
3 1 9 0 8 3 5 1 3 3
4 0 9 3 0 2 6 5 9 4
5 4 0 0 2 4 4 0 8 5
6 4 1 7 6 3 0 1 6 6
7 3 2 4 6 4 7 3 3 7
8 7 2 4 6 7 1 5 9 8
9 8 0 7 5 7 9 7 9 9