I have two DataFrames with one common column as the key and I want to perform a VLOOKUP sort of operation to fetch values from the first DataFrame corresponding to the keys in second DataFrame.
DataFrame 1
key value
0 aaa 111
1 bbb 222
2 ccc 333
DataFrame 2
key value
0 bbb None
1 ccc 333
2 aaa None
3 aaa 111
Desired Output
key value
0 bbb 222
1 ccc 333
2 aaa 111
3 aaa 111
I do not want to use merge as both of my DFs might have NULL values for the key column and since pandas merge behave differently than sql join, all such rows might get joined with each other.
I tried below approach
DF2['value'] = np.where(DF2['key'].isnull(), DF1.loc[DF2['key'].equals(DF1['key'])]['value'], DF2['value'])
but have been getting KeyError: False error.
You can use:
df2['value'] = df2['value'].fillna(df2['key'].map(df1.set_index('key')['value']))
print(df2)
# Output
key value
0 bbb 222
1 ccc 333
2 aaa 111
3 aaa 111
Related
there are similar questions existing, however cant find the right answer. Most of them require a common nominator which I don't have.
I want to have two outcomes from two data frames.
One is to get the percentage for each row in df2 from the total (df1). And another view of accumulated percentage.
df1
a
1875
df2
b c
aaa 125
bbb 250
ccc 500
ddd 1000
Required outcome.
b c Outcome 1 Outcome 2
aaa 125 6.67% 100.00%
bbb 250 13.33% 93.33%
ccc 500 26.67% 80.00%
ddd 1000 53.33% 53.33%
I have tried df1.eq(df2.values).mean() and couple of the merge functions. But again, don't have a common nominator.
Hope this helps. Thanks.
Use:
#get scalar from first DataFrame
a = df1.loc[0, 'a']
#divide by scalar and multiple by 100
df['Outcome 1'] = df['c'].div(a).mul(100)
#create cumulative sum in swapped order of rows
df['Outcome 2'] = df['Outcome 1'].iloc[::-1].cumsum()
print (df)
b c Outcome 1 Outcome 2
0 aaa 125 6.666667 100.000000
1 bbb 250 13.333333 93.333333
2 ccc 500 26.666667 80.000000
3 ddd 1000 53.333333 53.333333
I have a dataframe with 100s of columns and 1000s of rows but the basic structure is
Index 0 1 2
0 AAA NaN AAA
1 NaN BBB NaN
2 NaN NaN CCC
3 DDD DDD DDD
I would like to add two new columns one would be and id which would be equal to the first value in each row the second would be a count of the values in each row. It would look like this. To be clear all rows will always have the same value.
Index id count 0 1 2
0 AAA 2 AAA NaN AAA
1 BBB 1 NaN BBB NaN
2 CCC 1 NaN NaN CCC
3 DDD 3 DDD DDD DDD
Any help in figuring out a way to do this would be greatly appreciated. Thanks
This should work.
df['id'] = df.bfill(axis=1).iloc[:, 0].fillna('All NANs')
df['count'] = df.drop(columns=["id"]).notnull().sum(axis=1)
To maintain the order of columns:
df = df[list(df.columns[-2:]) + list(df.columns[:-2])]
Create the Dataframe
test_df = pd.DataFrame([['AAA',np.nan,'AAA'], [np.nan,'BBB',np.nan], [np.nan,np.nan, 'CCC'], ['DDD','DDD','DDD']])
Count the non-NaN elements in each row as count
test_df['count'] = test_df.notna().sum(axis=1)
Option-1: Select the first element in the row as id (regardless of NaN value)
test_df['id'] = test_df[0]
Option-2: Select the first non-NaN element as id for each row
test_df['id'] = test_df.apply(lambda x: x[x.first_valid_index()], axis=1)
I have 2 dataframes which look like this:
df1
A B
AAA 50
BBB 100
CCC 200
df2
C D
CCC 500
AAA 10
EEE 2100
I am trying to output the dataset where column E would be B - D if A = C. Since A values are not aligned with C values I cant seem to find the appropriate method to apply calculations and compare the right numbers.
There also are values which are not shared between two datasets in this case I want to add text value 'not found' in those places so that the output would look like this:
output
A B C D E
AAA 50 AAA 10 B-D
BBB 100 Not found Not found Not found
CCC 200 CCC 500 B-D
Not found Not found EEE 2100 Not found
Thank you for your suggestions.
Use outer join with left_on and right_on parameters with DataFrame.merge and then subtract columns, for possible subtract numeric is better use missing values:
df = (df1.merge(df2, left_on='A', right_on='C', how='outer')
.fillna({'A':'Not found', 'C':'Not found'})
.assign(E = lambda x: x.B - x.D))
print (df)
A B C D E
0 AAA 50.0 AAA 10.0 40.0
1 BBB 100.0 Not found NaN NaN
2 CCC 200.0 CCC 500.0 -300.0
3 Not found NaN EEE 2100.0 NaN
Last is possible replace all missing values, only numeric columns are now mixed - strings with numbers, so next processing like some arithmetic operations is problematic:
df = (df1.merge(df2, left_on='A', right_on='C', how='outer')
.assign(E = lambda x: x.B - x.D)
.fillna('Not found'))
print (df)
A B C D E
0 AAA 50 AAA 10 40
1 BBB 100 Not found Not found Not found
2 CCC 200 CCC 500 -300
3 Not found Not found EEE 2100 Not found
I am new to pandas and I am trying to get a list of values that exists in both columns, values that exist in column A, values that only exist in column B.
My .csv file looks like this:
A B
AAA ZZZ
BBB BBB
CCC EEE
DDD FFF
EEE AAA
DDD
GGG HHH
JJJ
Columns have a different length and my outcome would be 3 lists or one csv that I would ouput having 3 columns, one for items existing in both columns, one for items existing in only A column and one for items existing in only B column.
IN BOTH IN COLUMN A IN COLUMN B
AAA CCC ZZZ
BBB GGG FFF
DDD JJJ HHH
EEE
(empty one)
I have tried using .isin() module but it returns true of false rather than the actual list.
existing_in_both = df_column_a.isin(df_column_b)
And I do not know how I should try to extract values that only exist in either column A or B.
Thank you for your suggestions.
My actual .csv has the following:
id clickout_id timestamp click_id click_type
1 123abc 2019-11-25 c51c56d1 1
1 123dce 2019-11-25 c51c5fs1 12
and other file is looking like this:
timestamp id gid type
2019-11-25 1 c51c56d1 2
2019-11-25 1 c51c5fs1 2
And I am trying to compare click_id from first file and gid from the second file.
When I print out using your answer I get the header names as answers rather than the values from the columns.
Use sets with intersection and difference, then for new DataFrame are used Series, because different lengths of outputs:
a = set(df.A)
b = set(df.B)
df = pd.DataFrame({'IN BOTH': pd.Series(list(a & b)),
'IN COLUMN A': pd.Series(list(a - b)),
'IN COLUMN B': pd.Series(list(b - a))})
print (df)
IN BOTH IN COLUMN A IN COLUMN B
0 DDD CCC FFF
1 BBB GGG ZZZ
2 AAA JJJ HHH
3 NaN NaN
4 EEE NaN NaN
Or use numpy.intersect1d with numpy.setdiff1d:
df = pd.DataFrame({'IN BOTH': pd.Series(np.intersect1d(df.A, df.B)),
'IN COLUMN A': pd.Series(np.setdiff1d(df.A, df.B)),
'IN COLUMN B': pd.Series(np.setdiff1d(df.B, df.A))})
print (df)
IN BOTH IN COLUMN A IN COLUMN B
0 CCC FFF
1 AAA GGG HHH
2 BBB JJJ ZZZ
3 DDD NaN NaN
4 EEE NaN NaN
I have an existing Pandas Data-frame that I want to manipulate according to the following pattern:
The existing table has different set of codes in column 'code'. Each 'code' has certain labels listed in column 'label'. Each label has been tagged with either 0 or 1.
I have a requirement to add a 'new_column' with values 0 or 1 for each set of 'code', depending on the following condition:
Fill 1 in the 'new_column' only when all the 'label' of a particular 'code'
has value equals to 1 in the 'tag' column. Note I need to fill 1 for all the rows belonging to that particular 'code'.
As Shown in the desired Table, only code=30 has all the 'label' set in the 'tag' column equals to 1. Therefore i set the 'new_column' equals to 1 for that particular code. Rest of the codes have set to 0 value.
Existing Table:
code label tag
0 10 AAA 0
1 10 BBB 1
2 10 CCC 0
3 10 DDD 0
4 10 EEE 0
5 20 AAA 1
6 20 CCC 0
7 20 DDD 1
8 30 BBB 1
9 30 CCC 1
10 30 EEE 1
Desired Table
code label tag new_column
0 10 AAA 0 0
1 10 BBB 1 0
2 10 CCC 0 0
3 10 DDD 0 0
4 10 EEE 0 0
5 20 AAA 1 0
6 20 CCC 0 0
7 20 DDD 1 0
8 30 BBB 1 1
9 30 CCC 1 1
10 30 EEE 1 1
I have not tried any solution yet as it seems beyond my present level of expertise.
I think the right answer for this question is that given by #user3483203 in the comments:
df['new_column'] = df.groupby('code')['tag'].transform(all).astype(int)
The transform method applies to the dataframe whatever is passed to it, keeping the axis length the same.
The simple example in the documentation clearly explains the usage.
Coming to this particular question, the following happens when you run this snippet:
You first perform the grouping with respect to the 'code'. You end up with a DataFrameGroupBy object.
Next, from this you choose the tag column, ending up with a SeriesGroupBy object.
To this grouping, you apply the all function via transform, ultimately typecasting the boolean values to type int.
Basically, you can understand it like this (the values are binary to make them more related to your answer):
>>> int(all([1, 1, 1, 1]))
1
>>> int(all([1, 0, 1, 1]))
0
Finally, you are assigning the column you just created to the column new_column to the old dataframe.
the initial answer by user3483203 works. here is a variation. but his way was more concise.