I am trying to merge two Excel tables, but the rows don't line up because in one column information is split over several rows whereas in the other table it is contained in a single cell.
Is there a way with pandas to rename the cells in Table A so that they line up with the rows in Table B?
df_jobs = pd.read_excel(r"jobs.xlsx", usecols="Jobs")
df_positions = pd.read_excel(r"orders.xlsx", usecols="Orders")
Sample files:
https://drive.google.com/file/d/1PEG3nZc0183Gh-8A2xbIs9kEZIWLzLSa/view?usp=sharing
https://drive.google.com/file/d/1HfQ4q7pjba0TKNJAHBqcGeoqdY3Yr3DB/view?usp=sharing
I suppose your input data looks like:
>>> df1
A i j
0 O-20-003049 NaN NaN
1 1 0.643284 0.834937
2 2 0.056463 0.394168
3 3 0.773379 0.057465
4 4 0.081585 0.178991
5 5 0.667667 0.004370
6 6 0.672313 0.587615
7 O-20-003104 NaN NaN
8 1 0.916426 0.739700
9 O-20-003117 NaN NaN
10 1 0.800776 0.614192
11 2 0.925186 0.980913
12 3 0.503419 0.775606
>>> df2
A x y
0 O-20-003049.01 0.593312 0.666600
1 O-20-003049.02 0.554129 0.435650
2 O-20-003049.03 0.900707 0.623963
3 O-20-003049.04 0.023075 0.445153
4 O-20-003049.05 0.307908 0.503038
5 O-20-003049.06 0.844624 0.710027
6 O-20-003104.01 0.026914 0.091458
7 O-20-003117.01 0.275906 0.398993
8 O-20-003117.02 0.101117 0.691897
9 O-20-003117.03 0.739183 0.213401
We start by renaming the rows in column A:
# create a boolean mask
mask = df1["A"].str.startswith("O-")
# rename all rows
df1["A"] = df1.loc[mask, "A"].reindex(df1.index).ffill() \
+ "." + df1["A"].str.pad(2, fillchar="0")
# remove unwanted rows (where mask==True)
df1 = df1[~mask].reset_index(drop=True)
>>> df1
A i j
1 O-20-003049.01 0.000908 0.078590
2 O-20-003049.02 0.896207 0.406293
3 O-20-003049.03 0.120693 0.722355
4 O-20-003049.04 0.412412 0.447349
5 O-20-003049.05 0.369486 0.872241
6 O-20-003049.06 0.614941 0.907893
8 O-20-003104.01 0.519443 0.800131
10 O-20-003117.01 0.583067 0.760002
11 O-20-003117.02 0.133029 0.389461
12 O-20-003117.03 0.969289 0.397733
Now, we are able to merge data on column A:
>>> pd.merge(df1, df2, on="A")
A i j x y
0 O-20-003049.01 0.643284 0.834937 0.593312 0.666600
1 O-20-003049.02 0.056463 0.394168 0.554129 0.435650
2 O-20-003049.03 0.773379 0.057465 0.900707 0.623963
3 O-20-003049.04 0.081585 0.178991 0.023075 0.445153
4 O-20-003049.05 0.667667 0.004370 0.307908 0.503038
5 O-20-003049.06 0.672313 0.587615 0.844624 0.710027
6 O-20-003104.01 0.916426 0.739700 0.026914 0.091458
7 O-20-003117.01 0.800776 0.614192 0.275906 0.398993
8 O-20-003117.02 0.925186 0.980913 0.101117 0.691897
9 O-20-003117.03 0.503419 0.775606 0.739183 0.213401
Related
I have two csv files: one containing data, the other one containing a single row with the same columns as the first file. I am trying to subtract the one row from the second file from all the rows from the first file using pandas.
I have tried the following, but to no avail.
df = df.subtract(row, axis=1)
You're looking for the "drop" method. From pandas docs:
df
A B C D
0 0 1 2 3
1 4 5 6 7
2 8 9 10 11
drop by index:
df.drop([0, 1])
A B C D
2 8 9 10 11
https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.drop.html
I have a data frame which looks like this
GROUP_FIELD_NAME:BKR_ID
GROUP_FIELD_VALUE:T80
GROUP_FIELD_NAME:GROUP_OFFSET
GROUP_FIELD_VALUE:0
GROUP_FIELD_NAME:GROUP_LENGTH
GROUP_FIELD_VALUE:0
GROUP_FIELD_NAME:FIRM_ID
GROUP_FIELD_VALUE:KIZEM
GROUP_FILENAME:000000018.pdf
GROUP_FIELD_NAME:BKR_ID
GROUP_FIELD_VALUE:T80
GROUP_FIELD_VALUE:P
GROUP_FIELD_NAME:FI_ID
GROUP_FIELD_VALUE:
GROUP_FIELD_NAME:RUN_DTE
GROUP_FIELD_VALUE:20220208
GROUP_FIELD_NAME:FIRM_ID
GROUP_FIELD_VALUE:KIZEM
GROUP_FILENAME:000000019.pdf
It has three keys Group field ,group field value and group file name,i want to create a dataframe like this
I am expecting a data frame with three column group_field_name,group_field_value and group_file name.
You can use:
(df['col'].str.extract('GROUP_FILENAME:(.*)|([^:]+):(.*)')
.set_axis(['GROUP_FILENAME', 'var', 'val'], axis=1)
.assign(GROUP_FILENAME=lambda d: d['GROUP_FILENAME'].bfill(),
n=lambda d: d.groupby(['GROUP_FILENAME', 'var']).cumcount()
)
.dropna(subset=['var'])
.pivot(index=['GROUP_FILENAME', 'n'], columns='var', values='val')
.droplevel(1).rename_axis(columns=None)
.reset_index('GROUP_FILENAME')
)
Output:
GROUP_FILENAME GROUP_FIELD_NAME GROUP_FIELD_VALUE
0 000000018.pdf BKR_ID T80
1 000000018.pdf GROUP_OFFSET 0
2 000000018.pdf GROUP_LENGTH 0
3 000000018.pdf FIRM_ID KIZEM
4 000000019.pdf BKR_ID T80
5 000000019.pdf FI_ID P
6 000000019.pdf RUN_DTE
7 000000019.pdf FIRM_ID 20220208
8 000000019.pdf NaN KIZEM
Used input:
col
0 GROUP_FIELD_NAME:BKR_ID
1 GROUP_FIELD_VALUE:T80
2 GROUP_FIELD_NAME:GROUP_OFFSET
3 GROUP_FIELD_VALUE:0
4 GROUP_FIELD_NAME:GROUP_LENGTH
5 GROUP_FIELD_VALUE:0
6 GROUP_FIELD_NAME:FIRM_ID
7 GROUP_FIELD_VALUE:KIZEM
8 GROUP_FILENAME:000000018.pdf
9 GROUP_FIELD_NAME:BKR_ID
10 GROUP_FIELD_VALUE:T80
11 GROUP_FIELD_VALUE:P
12 GROUP_FIELD_NAME:FI_ID
13 GROUP_FIELD_VALUE:
14 GROUP_FIELD_NAME:RUN_DTE
15 GROUP_FIELD_VALUE:20220208
16 GROUP_FIELD_NAME:FIRM_ID
17 GROUP_FIELD_VALUE:KIZEM
18 GROUP_FILENAME:000000019.pdf
If we have two columns A and B
how to compare row A with last row B
A B diff
0 0.904560 0.208318 0
1 0.679290 0.747496 0
2 0.069841 0.165834 0
3 0.045818 0.907888 0
4 0.485712 0.593785 0
5 0.771665 0.800182 0
6 0.485041 0.024829 0
7 0.897172 0.584406 0
8 0.561953 0.626699 0
9 0.412803 0.900643 0
You can use Pandas' shift function to create a 'lagged' version of column B. Then it's a simple difference between columns.
from io import StringIO
import pandas as pd
raw = '''
A,B
0.904560,0.208318
0.679290,0.747496
0.069841,0.165834
0.045818,0.907888
0.485712,0.593785
0.771665,0.800182
0.485041,0.024829
0.897172,0.584406
0.561953,0.626699
0.412803,0.900643
'''.strip()
df = pd.read_csv(StringIO(raw))
df['B_lag'] = df.B.shift(1)
df['diff'] = df.A - df.B_lag
print(df)
Output looks like
A B B_lag diff
0 0.904560 0.208318 NaN NaN
1 0.679290 0.747496 0.208318 0.470972
2 0.069841 0.165834 0.747496 -0.677655
3 0.045818 0.907888 0.165834 -0.120016
4 0.485712 0.593785 0.907888 -0.422176
5 0.771665 0.800182 0.593785 0.177880
6 0.485041 0.024829 0.800182 -0.315141
7 0.897172 0.584406 0.024829 0.872343
8 0.561953 0.626699 0.584406 -0.022453
9 0.412803 0.900643 0.626699 -0.213896
Objective: to lookup value from one data frame (conditionally) and place the results in a different dataframe with a new column name
df_1 = pd.DataFrame({'user_id': [1,2,1,4,5],
'name': ['abc','def','ghi','abc','abc'],
'rank': [6,7,8,9,10]})
df_2 = pd.DataFrame ({'user_id': [1,2,3,4,5]})
df_1 # original data
df_2 # new dataframe
In this general example, I am trying to create a new column named "priority_rank" and only fill "priority_rank" based on the conditional lookup against df_1, namely the following:
user_id must match between df_1 and df_2
I am interested in only df_1['name'] == 'abc' all else should be blank
df_2 should end up looking like this:
|user_id|priority_rank|
1 6
2
3
4 9
5 10
One way to do this:
In []:
df_2['priority_rank'] = np.where((df_1.name=='abc') & (df_1.user_id==df_2.user_id), df_1['rank'], '')
df_2
Out[]:
user_id priority_rank
0 1 6
1 2
2 3
3 4 9
4 5 10
Note: In your example df_1.name=='abc' is a sufficient condition because all values for user_id are identical when df_1.name=='abc'. I'm assuming this is not always going to be the case.
Using merge
df_2.merge(df_1.loc[df_1.name=='abc',:],how='left').drop('name',1)
Out[932]:
user_id rank
0 1 6.0
1 2 NaN
2 3 NaN
3 4 9.0
4 5 10.0
You're looking for map:
df_2.assign(priority_rank=df_2['user_id'].map(
df_1.query("name == 'abc'").set_index('user_id')['rank']))
user_id priority_rank
0 1 6.0
1 2 NaN
2 3 NaN
3 4 9.0
4 5 10.0
I know how to set the pandas data frame equal to a column.
i.e.:
df = df['col1']
what is the equivalent for a row? let's say taking the index? and would I eliminate one or more of them?
Many thanks.
If you want to take a copy of a row then you can either use loc for label indexing or iloc for integer based indexing:
In [104]:
df = pd.DataFrame({'a':np.random.randn(10),'b':np.random.randn(10)})
df
Out[104]:
a b
0 1.216387 -1.298502
1 1.043843 0.379970
2 0.114923 -0.125396
3 0.531293 -0.386598
4 -0.278565 1.224272
5 0.491417 -0.498816
6 0.222941 0.183743
7 0.322535 -0.510449
8 0.695988 -0.300045
9 -0.904195 -1.226186
In [106]:
row = df.iloc[3]
row
Out[106]:
a 0.531293
b -0.386598
Name: 3, dtype: float64
If you want to remove that row then you can use drop:
In [107]:
df.drop(3)
Out[107]:
a b
0 1.216387 -1.298502
1 1.043843 0.379970
2 0.114923 -0.125396
4 -0.278565 1.224272
5 0.491417 -0.498816
6 0.222941 0.183743
7 0.322535 -0.510449
8 0.695988 -0.300045
9 -0.904195 -1.226186
You can also use a slice or pass a list of labels:
In [109]:
rows = df.loc[[3,5]]
row_slice = df.loc[3:5]
print(rows)
print(row_slice)
a b
3 0.531293 -0.386598
5 0.491417 -0.498816
a b
3 0.531293 -0.386598
4 -0.278565 1.224272
5 0.491417 -0.498816
Similarly you can pass a list to drop:
In [110]:
df.drop([3,5])
Out[110]:
a b
0 1.216387 -1.298502
1 1.043843 0.379970
2 0.114923 -0.125396
4 -0.278565 1.224272
6 0.222941 0.183743
7 0.322535 -0.510449
8 0.695988 -0.300045
9 -0.904195 -1.226186
If you wanted to drop a slice then you can slice your index and pass this to drop:
In [112]:
df.drop(df.index[3:5])
Out[112]:
a b
0 1.216387 -1.298502
1 1.043843 0.379970
2 0.114923 -0.125396
5 0.491417 -0.498816
6 0.222941 0.183743
7 0.322535 -0.510449
8 0.695988 -0.300045
9 -0.904195 -1.226186