I'm trying to get data from a PostgreSQL database using ADO and official PostgreSQL ODBC driver. In some cases I get wrong field names when using NextRecordset(). It looks like a bug in a driver. Is there any workaround for this?
Here is a small example. It prints 'g1' for the last field of the second recordset, but it must be 'g2'.
SQL
Create Table Test ("Field" int);
C++
_bstr_t strCnn("Provider='MSDASQL';Driver=PostgreSQL Unicode;uid=postgres;Server=127.0.0.1;port=5432;database=MyDB;pwd=password;");
_RecordsetPtr pRstCompound = NULL;
TESTHR(pRstCompound.CreateInstance(__uuidof(Recordset)));
auto Statement =
"Select '1' a1, '2' b1, '3' c1, '4' d1, '5' e1, '6' f1, \"Field\" g1 From Test;\n"
"Select '1' a2, '2' b2, '3' c2, '4' d2, '5' e2, \"Field\" f2, '7' g2 From Test;\n";
pRstCompound->Open(Statement, strCnn, adOpenForwardOnly, adLockReadOnly, adCmdText);
int intCount = 1;
while (!(pRstCompound == NULL)) {
printf("\n\nContents of recordset #%d\n", intCount++);
auto Fields = pRstCompound->Fields;
long const nFields = Fields->Count;
for (long nField = 0; nField < nFields; ++nField)
printf("%s%s",
(LPCSTR)(_bstr_t)Fields->GetItem(nField)->Name,
nField + 1 == nFields ? "\n" : "\t");
pRstCompound = pRstCompound->NextRecordset(nullptr);
}
Output:
Contents of recordset #1
a1 b1 c1 d1 e1 f1 g1
Contents of recordset #2
a2 b2 c2 d2 e2 f2 g1
Expected output:
Contents of recordset #1
a1 b1 c1 d1 e1 f1 g1
Contents of recordset #2
a2 b2 c2 d2 e2 f2 g2
I've .xlsx file. Rows are good, values are just fine. But i need to change the columns order by list of new columns positions, e.g:
old = [1, 2, 3, 4]
new = [2, 1, 4, 3]
Docs are checked - there is no straightforward options for this problem.
I've tried to iterate over columns, so:
old = {cell.column: cell.value for cell in ws[1]}.keys() # [1, 2, 3, 4]
new = [2, 1, 4, 3]
for i, col in enumerate(ws.iter_cols(max_col=old[-1]), 1):
if old[i-1] != new[i-1]:
for one in ws[get_column_letter(i)]:
old_cell = one
new_cell = ws.cell(old_cell.row, old[new[i-1]]-1)
new_cell.value, old_cell.value = old_cell.value, new_cell.value
old[i] = new_cell.column
old[new_cell.column] = old_cell.column
...but it work only for a few cases. Probably i'm missing some general solution.
At the and it should be, for example, old = [1, 2, 3, 4] new = [2, 1, 4, 3]:
Input file:
x A B C D
1 A1 B1 C1 D1
2 A2 B2 C2 D2
3 A3 B3 C3 D3
4 A4 B4 C3 D4
Output file:
x A B C D
1 B1 A1 D1 C1
2 B2 A2 D2 C2
3 B3 A3 D3 C3
4 B4 A4 D3 C4
Your current approach risks overwriting cells. I'd be tempted to move the cells from existing columns to new columns in the correct order and then delete the old ones.
for c in ws['A']:
new_cell = c.offset(column=6)
new_cell.value = c.value
for c in ws['B']:
new_cell = c.offset(column=5)
new_cell.value = c.value
ws.delete_cols(min_col=1, max_col=4)
This is just to give you the general idea could be optimised and parametrised so you could do each row at once.
Or you could use move_range:
ws.move_range(min_col=1, max_col=1, min_row=1, max_row=ws.max_row, cols=5)
In either case be careful not to overwrite existing cells before you've moved them.
How do I write the formula for the following:
if B2 = "SF" and D2 = "1"
then H2 = E2 + .75
else if B2 = "SF" and D2 = ".25"
then H2 = E2 + .625
else if B2 = "CW" and D2 = "1"
then H2 = E2 + 1
I want my answers to be in H2, with data being entered into B2, D2 and E2.
=if(AND(B2="SF",D2=1)=TRUE,E2+0.75,if(AND(B2="SF",D2=0.25)=TRUE,E2+0.625,if(AND(B2="CW",D2=1)=TRUE,E2+1,0)))
Regards
I have a csv-file that contains "pivot-like" data that I would like to store into a pandas DataFrame. The original data file is divided using different number of whitespaces to differentiate between the level in the pivot-data like so:
Text that I do not want to include,,
,Text that I do not want to include,Text that I do not want to include
,header A,header B
Total,100,100
A,,2.15
a1,,2.15
B,,0.22
b1,,0.22
" slightly longer name"...,,0.22
b3,,0.22
C,71.08,91.01
c1,57.34,73.31
c2,5.34,6.76
c3,1.33,1.67
x1,0.26,0.33
x2,0.26,0.34
x3,0.48,0.58
x4,0.33,0.42
c4,3.52,4.33
x5,0.27,0.35
x6,0.21,0.27
x7,0.49,0.56
x8,0.44,0.47
x9,0.15,0.19
x10,,0.11
x11,0.18,0.23
x12,0.18,0.23
x13,0.67,0.85
x14,0.24,0.2
x15,0.68,0.87
c5,0.48,0.76
x16,,0.15
x17,0.3,0.38
x18,0.18,0.23
d2,6.75,8.68
d3,0.81,1.06
x19,0.3,0.38
x20,0.51,0.68
Others,24.23,0
N/A,,
"Text that I do not want to include(""at all"") ",,
(It looks aweful, but you should be able to paste in e.g. Notepad to see it a bit clearer)
Basically, there are only two columns a and b, but the rows are indented using 0, 3, 6, 9, ... etc whitespaces to differentiate between the levels. So for instance,
zero level, the main group, A has 0 spaces,
first level a1 has 3 spaces,
second level a2 has 6 spaces,
third level a3 has 9 spaces and
fourth and final level has 12 spaces with the corresponding values for columns a and b respectively.
I would now like to be able to read and group this data on these levels in order to create a new summarizing DataFrame, with columns corresponding to these different levels, looking like:
Level 4 Diff(a,b) Level 0 Level 1 Level 2 Level 3
x7 525 C c1 c2 c3
x5 -0.03 A a1 a22 NaN
x4 -0.04 A a1 a22 NaN
x8 -0.08 C c1 c2 c3
…
Any clue on how to do this?
Thanks
Easiest is to split this into different functions
read the file
parse the lines
generate the 'tree'
construct the DataFrame
Parse the lines
def parse_file(file):
import ast
import re
pat = re.compile(r'^( *)(\w+),([\d.]+),([\d.]+)$')
for line in file:
r = pat.match(line)
if r:
spaces, label, a, b = r.groups()
diff = ast.literal_eval(a) - ast.literal_eval(b)
yield len(spaces)//3, label, diff
Reads each line, yields the level, 'label' and diff using a regular expression. I use ast to convert the string to int or float
Generate the tree
def parse_lines(lines):
previous_label = list(range(5))
for level, label, diff in lines:
previous_label[level] = label
if level == 4:
yield tuple(previous_label), diff
Initiates a list of length 5, and then overwrites the level this node is on.
Construct the DataFrame
with StringIO(file_content) as file:
lines = parse_file(file)
index, data = zip(*parse_lines(lines))
idx = pd.MultiIndex.from_tuples(index, names=[f'level_{i}' for i in range(len(index[0]))])
df = pd.DataFrame(data={'Diff(a,b)': list(data)}, index=idx)
Opens the file, constructs the index and generates the DataFrame with the different levels in the index. If you don't want this, you can add a .reset_index() or construct the DataFrame slightly different
df
level_0 level_1 level_2 level_3 level_4 Diff(a,b)
A a1 a2 a3 x1 -0.07
A a1 a2 a3 x2 -0.08000000000000002
A a1 a22 a3 x3 -0.04999999999999999
A a1 a22 a3 x4 -0.04000000000000001
A a1 a22 a3 x5 -0.03
A a1 a22 a3 x6 -0.06999999999999998
C c1 c2 c3 x7 525.0
C c1 c2 c3 x8 -0.08000000000000002
alternative for missing levels
def parse_lines(lines):
labels = [None] * 5
previous_level = None
for level, label, diff in lines:
labels[level] = label
if level == 4:
if previous_level < 3:
labels = labels[:previous_level + 1] + [None] * (5 - previous_level)
labels[level] = label
yield tuple(labels), diff
previous_level = level
the items under a22 don't seem to have a level_3, so it copies that from the previous. If this is unwanted, you can take this variation
df
level_0 level_1 level_2 level_3 level_4 Diff(a,b)
C c1 c2 c3 x1 -0.07
C c1 c2 c3 x2 -0.08000000000000002
C c1 c2 c3 x3 -0.09999999999999998
C c1 c2 c3 x4 -0.08999999999999997
C c1 c2 c4 x5 -0.07999999999999996
C c1 c2 c4 x6 -0.060000000000000026
C c1 c2 c4 x7 -0.07000000000000006
C c1 c2 c4 x8 -0.02999999999999997
C c1 c2 c4 x9 -0.04000000000000001
C c1 c2 c4 x11 -0.05000000000000002
C c1 c2 c4 x12 -0.05000000000000002
C c1 c2 c4 x13 -0.17999999999999994
C c1 c2 c4 x14 0.03999999999999998
C c1 c2 c4 x15 -0.18999999999999995
C c1 c2 c5 x17 -0.08000000000000002
C c1 c2 c5 x18 -0.05000000000000002
C c1 d2 d3 x19 -0.08000000000000002
C c1 d2 d3 x20 -0.17000000000000004
I have two Pandas data frames and they need to be merged. Example data frames are:
c1 c2
pd1 = [[1, [1,2]]
c3 c4
pd2 = [[1, [1,3]],
[2,[2,3]]
result = [[1,1], [1,2]]
The join condition is that lists in c2 and c4 have at lease one common element.
I've tried:
result = pd.merge(pd1, pd2, left_on=list('c2'),right_on=list('c4'), how='inner')
However, this seems to only join them when the rows in each column are single values like a float, int or string.
I've attacked this problem using nested loops. This runs like a dog when the sets get large. Is there a faster way to perform this merge exploiting data frames or is there another way that's better?
pd1 = pd.DataFrame([[1, [1,2]]], columns=['c1', 'c2'])
pd1
pd2 = pd.DataFrame([[1, [1, 2]], [2, [2, 3]]], columns=['c3', 'c4'])
pd2
Setup for a merge
s2 = pd2.c4.apply(pd.Series).stack() \
.rename_axis(['idx2', 'lst2']).reset_index(name='val')
s2
s1 = pd1.c2.apply(pd.Series).stack() \
.rename_axis(['idx1', 'lst1']).reset_index(name='val')
s1
mrg = s1.merge(s2)[['idx1', 'idx2']].drop_duplicates()
mrg
a1 = pd1.c1.loc[mrg.idx1].values
a2 = pd2.c3.loc[mrg.idx2]
pd.DataFrame(dict(c1=a1, c3=a2))