Here is my dataframe:
Date cell tumor_size(mm)
25/10/2015 113 51
22/10/2015 222 50
22/10/2015 883 45
20/10/2015 334 35
19/10/2015 564 47
19/10/2015 123 56
22/10/2014 345 36
13/12/2013 456 44
What I want to do is compare the size of the tumors detected on the different days. Let's consider the cell 222 as an example; I want to compare its size to different cells but detected on earlier days e.g. I will not compare its size with cell 883, because they were detected on the same day. Or I will not compare it with cell 113, because it was detected later on.
As my dataset is too large, I have iterate over the rows. If I explain it in a non-pythonic way:
for the cell 222:
get_size_distance(absolute value):
(50 - 35 = 15), (50 - 47 = 3), (50 - 56 = 6), (50 - 36 = 14), (44 - 36 = 8)
get_minumum = 3, I got this value when I compared it with 564, so I will name it as a pait for the cell 222
Then do it for the cell 883
The resulting output should look like this:
Date cell tumor_size(mm) pair size_difference
25/10/2015 113 51 222 1
22/10/2015 222 50 123 6
22/10/2015 883 45 456 1
20/10/2015 334 35 345 1
19/10/2015 564 47 456 3
19/10/2015 123 56 456 12
22/10/2014 345 36 456 8
13/12/2013 456 44 NaN NaN
I will really appreciate your help
It's not pretty, but I believe it does the trick
a = pd.read_clipboard()
# Cut off last row since it was a faulty date. You can skip this.
df = a.copy().iloc[:-1]
# Convert to dates and order just in case (not really needed I guess).
df['Date'] = df.Date.apply(lambda x: datetime.strptime(x, '%d/%m/%Y'))
df.sort_values('Date', ascending=False)
# Rename column
df = df.rename(columns={"tumor_size(mm)": 'tumor_size'})
# These will be our lists of pairs and size differences.
pairs = []
diffs = []
# Loop over all unique dates
for date in df.Date.unique():
# Only take dates earlier then current date.
compare_df = df.loc[df.Date < date].copy()
# Loop over each cell for this date and find the minimum
for row in df.loc[df.Date == date].itertuples():
# If no cells earlier are available use nans.
if compare_df.empty:
pairs.append(float('nan'))
diffs.append(float('nan'))
# Take lowest absolute value and fill in otherwise
else:
compare_df['size_diff'] = abs(compare_df.tumor_size - row.tumor_size)
row_of_interest = compare_df.loc[compare_df.size_diff == compare_df.size_diff.min()]
pairs.append(row_of_interest.cell.values[0])
diffs.append(row_of_interest.size_diff.values[0])
df['pair'] = pairs
df['size_difference'] = diffs
returns:
Date cell tumor_size pair size_difference
0 2015-10-25 113 51 222.0 1.0
1 2015-10-22 222 50 564.0 3.0
2 2015-10-22 883 45 564.0 2.0
3 2015-10-20 334 35 345.0 1.0
4 2015-10-19 564 47 345.0 11.0
5 2015-10-19 123 56 345.0 20.0
6 2014-10-22 345 36 NaN NaN
Related
High D_HIGH D_HIGH_H
33 46.57 0 0L
0 69.93 42 42H
1 86.44 68 68H
34 56.58 83 83L
35 67.12 125 125L
2 117.91 158 158H
36 94.51 186 186L
3 120.45 245 245H
4 123.28 254 254H
37 83.20 286 286L
In column D_HIGH_H there is L & H at end.
If there are two continuous H then the one having highest value in High column has to be selected and other has to be ignored(deleted).
If there are two continuous L then the one having lowest value in High column has to be selected and other has to be ignored(deleted).
If the sequence is H,L,H,L then no changes to be made.
Output I want is as follows:
High D_HIGH D_HIGH_H
33 46.57 0 0L
1 86.44 68 68H
34 56.58 83 83L
2 117.91 158 158H
36 94.51 186 186L
4 123.28 254 254H
37 83.20 286 286L
I tried various options using list map but did not work out.Also tried with groupby but no logical conclusion.
Here's one way:
g = ((l := df['D_HIGH_H'].str[-1]) != l.shift()).cumsum()
def f(x):
if (x['D_HIGH_H'].str[-1] == 'H').any():
return x.nlargest(1, 'D_HIGH')
return x.nsmallest(1, 'D_HIGH')
df.groupby(g, as_index=False).apply(f)
Output:
High D_HIGH D_HIGH_H
0 33 46.57 0 0L
1 1 86.44 68 68H
2 34 56.58 83 83L
3 2 117.91 158 158H
4 36 94.51 186 186L
5 4 123.28 254 254H
6 37 83.20 286 286L
You can use extract to get the letter, then compute a custom group and groupby.apply with a function that depends on the letter:
# extract letter
s = df['D_HIGH_H'].str.extract('(\D)$', expand=False)
# group by successive letters
# get the idxmin/idxmax depending on the type of letter
keep = (df['High']
.groupby([s, s.ne(s.shift()).cumsum()], sort=False)
.apply(lambda x: x.idxmin() if x.name[0] == 'L' else x.idxmax())
.tolist()
)
out = df.loc[keep]
Output:
High D_HIGH D_HIGH_H
33 46.57 0 0L
1 86.44 68 68H
34 56.58 83 83L
2 117.91 158 158H
36 94.51 186 186L
4 123.28 254 254H
37 83.20 286 286L
I have the following problem
import pandas as pd
data = {
"ID": [420, 380, 390, 540, 520, 50, 22],
"duration": [50, 40, 45,33,19,1,3],
"next":["390;50","880;222" ,"520;50" ,"380;111" ,"810;111" ,"22;888" ,"11" ]
}
#load data into a DataFrame object:
df = pd.DataFrame(data)
print(df)
As you can see I have
ID duration next
0 420 50 390;50
1 380 40 880;222
2 390 45 520;50
3 540 33 380;111
4 520 19 810;111
5 50 1 22;888
6 22 3 11
Things to notice:
ID type is int
next type is a string with numbers separated by ; if more than two numbers
I would like to filter the rows with no next in the ID
For example in this case
420 has a follow up in both 390 and 50
380 has as next 880 and 222 both of which are not in ID so this one
540 has as next 380 and 111 and while 111 is not in ID, 380 is so not this one
same with 50
In the end I want to get
1 380 40 880;222
4 520 19 810;111
6 22 3 11
With only one value I used print(df[~df.next.astype(int).isin(df.ID)]) but in this case isin can not be simply applied.
How can I do this?
Let us try with split then explode with isin check
s = df.next.str.split(';').explode().astype(int)
out = df[~s.isin(df['ID']).groupby(level=0).any()]
Out[420]:
ID duration next
1 380 40 880;222
4 520 19 810;111
6 22 3 11
Use a regex with word boundaries for efficiency:
pattern = '|'.join(df['ID'].astype(str))
out = df[~df['next'].str.contains(fr'\b(?:{pattern})\b')]
Output:
ID duration next
1 380 40 880;222
4 520 19 810;111
6 22 3 11
I have many blanks in a merged data set and I want to fill them with a condition.
My current code looks like this
import pandas as pd
import csv
import numpy as np
pd.set_option('display.max_columns', 500)
# Read all files into pandas dataframes
Jan = pd.read_csv(r'C:\~\Documents\Jan.csv')
Feb = pd.read_csv(r'C:\~\Documents\Feb.csv')
Mar = pd.read_csv(r'C:\~\Documents\Mar.csv')
Jan=pd.DataFrame({'Department':['52','5','56','70','7'],'Item':['2515','254','818','','']})
Feb=pd.DataFrame({'Department':['52','56','765','7','40'],'Item':['2515','818','524','','']})
Mar=pd.DataFrame({'Department':['7','70','5','8','52'],'Item':['45','','818','','']})
all_df_list = [Jan, Feb, Mar]
appended_df = pd.concat(all_df_list)
df = appended_df
df.to_csv(r"C:\~\Documents\SallesDS.csv", index=False)
Data set:
df
Department Item
52 2515
5 254
56 818
70
7 50
52 2515
56 818
765 524
7
40
7 45
70
5 818
8
52
What I want is to fill the empty cells in Item with a correspondent values of the Department column.
So If Department is 52 and Item is empty it should be filled with 2515
Department 7 and Item is empty fill it with 45
and the result should look like this
df
Department Item
52 2515
5 254
56 818
70
7 50
52 2515
56 818
765 524
7 45
40
7 45
70
5 818
8
52 2515
I tried the following method but non of them worked.
1
df.loc[(df['Item'].isna()) & (df['Department'].str.contains(52)), 'Item'] = 2515
df.loc[(df['Item'].isna()) & (df['Department'].str.contains(7)), 'Item'] = 45
2
df["Item"] = df["Item"].fillna(df["Department"])
df = df.replace({"Item":{"52":"2515", "7":"45"}})
both ethir return error or do not work
Answer:
Hi I have used the below code and it worked
b = [52]
df.Item=np.where(df.Department.isin(b),df.Item.fillna(2515),df.Item)
a = [7]
df.Item=np.where(df.Department.isin(a),df.Item.fillna(45),df.Item)
Hope it helps someone who face the same issue
The following solution first creates a map of each department and it's maximum corresponding item (assuming there is one), and then matches that item to a department with a blank item. Note that in your data frame, the empty items are an empty string ("") and not NaN.
Create a map:
values = df.groupby('Department').max()
values['Item'] = values['Item'].apply(lambda x: np.nan if x == "" else x)
values = values.dropna().reset_index()
Department Item
0 5 818
1 52 2515
2 56 818
3 7 45
4 765 524
Then use df.apply():
df['Item'] = df.apply(lambda x: values[values['Department'] == x['Department']]['Item'].values if x['Item'] == "" else x['Item'], axis=1)
In this case, the new values will have brackets around them. They can be removed with str.replace():
df['Item'] = df['Item'].astype(str).str.replace(r'\[|\'|\'|\]', "", regex=True)
The result:
Department Item
0 52 2515
1 5 254
2 56 818
3 70
4 7 45
0 52 2515
1 56 818
2 765 524
3 7 45
4 40
0 7 45
1 70
2 5 818
3 8
4 52 2515
Hi I have used the below code and it worked
b = [52]
df.Item=np.where(df.Department.isin(b),df.Item.fillna(2515),df.Item)
a = [7]
df.Item=np.where(df.Department.isin(a),df.Item.fillna(45),df.Item)
Hope it helps someone who face the same issue
I have the following dataframe for which I want to create a column named 'Value' using numpy for fast looping and at the same time refer to the previous row value in the same column.
import pandas as pd
import numpy as np
df = pd.DataFrame(
{
"Product": ["A", "A", "A", "A", "B", "B", "B", "C", "C"],
"Inbound": [115, 220, 200, 402, 313, 434, 321, 343, 120],
"Outbound": [10, 20, 24, 52, 40, 12, 43, 23, 16],
"Is First?": ["Yes", "No", "No", "No", "Yes", "No", "No", "Yes", "No"],
}
)
Product Inbound Outbound Is First? Value
0 A 115 10 Yes 125
1 A 220 20 No 105
2 A 200 24 No 81
3 A 402 52 No 29
4 B 313 40 Yes 353
5 B 434 12 No 341
6 B 321 43 No 298
7 C 343 23 Yes 366
8 C 120 16 No 350
The formula for Value column in pseudocode is:
if ['Is First?'] = 'Yes' then [Value] = [Inbound] + [Outbound]
else [Value] = [Previous Value] - [Outbound]
The ideal way of creating the Value column right now is to do a for loop and use shift to refer to the previous column (which I am somehow not able to make work). But since I will be applying this over a giant dataset, I want to use the numpy vectorization method on it.
for i in range(len(df)):
if df.loc[i, "Is First?"] == "Yes":
df.loc[i, "Value"] = df.loc[i, "Inbound"] + df.loc[i, "Outbound"]
else:
df.loc[i, "Value"] = df.loc[i, "Value"].shift(-1) + df.loc[i, "Outbound"]
One way:
You may use np.subtract.accumulate with transform
s = df['Is First?'].eq('Yes').cumsum()
df['value'] = ((df.Inbound + df.Outbound).where(df['Is First?'].eq('Yes'), df.Outbound)
.groupby(s)
.transform(np.subtract.accumulate))
Out[1749]:
Product Inbound Outbound Is First? value
0 A 115 10 Yes 125
1 A 220 20 No 105
2 A 200 24 No 81
3 A 402 52 No 29
4 B 313 40 Yes 353
5 B 434 12 No 341
6 B 321 43 No 298
7 C 343 23 Yes 366
8 C 120 16 No 350
Another way:
Assign value for Yes. Create groupid s to use for groupby. Groupby and shift Outbound to calculate cumsum, and subtract it from 'Yes' value of each group. Finally, use it to fillna.
df['value'] = (df.Inbound + df.Outbound).where(df['Is First?'].eq('Yes'))
s = df['Is First?'].eq('Yes').cumsum()
s1 = df.value.ffill() - df.Outbound.shift(-1).groupby(s).cumsum().shift()
df['value'] = df.value.fillna(s1)
Out[1671]:
Product Inbound Outbound Is First? value
0 A 115 10 Yes 125.0
1 A 220 20 No 105.0
2 A 200 24 No 81.0
3 A 402 52 No 29.0
4 B 313 40 Yes 353.0
5 B 434 12 No 341.0
6 B 321 43 No 298.0
7 C 343 23 Yes 366.0
8 C 120 16 No 350.0
This is not a trivial task, the difficulty lies in the consecutive Nos. It's necessary to group consecutive no's together, the code below should do,
col_sum = df.Inbound+df.Outbound
mask_no = df['Is First?'].eq('No')
mask_yes = df['Is First?'].eq('Yes')
consec_no = mask_yes.cumsum()
result = col_sum.groupby(consec_no).transform('first')-df['Outbound'].where(mask_no,0).groupby(consec_no).cumsum()
Use:
df.loc[df['Is First?'].eq('Yes'),'Value']=df['Inbound']+df['Outbound']
df.loc[~df['Is First?'].eq('Yes'),'Value']=df['Value'].fillna(0).shift().cumsum()-df.loc[~df['Is First?'].eq('Yes'),'Outbound'].cumsum()
Annotated numpy code:
## 1. line up values to sum
ob = -df["Outbound"].values
# get yes indices
fi, = np.where(df["Is First?"].values == "Yes")
# insert yes formula at yes positions
ob[fi] = df["Inbound"].values[fi] - ob[fi]
## 2. calculate block sums and subtract each from the
## first element of the **next** block
ob[fi[1:]] -= np.add.reduceat(ob,fi)[:-1]
# now simply taking the cumsum will reset after each block
df["Value"] = ob.cumsum()
Result:
Product Inbound Outbound Is First? Value
0 A 115 10 Yes 125
1 A 220 20 No 105
2 A 200 24 No 81
3 A 402 52 No 29
4 B 313 40 Yes 353
5 B 434 12 No 341
6 B 321 43 No 298
7 C 343 23 Yes 366
8 C 120 16 No 350
It is the first time I use pandas and I do not really know how to deal with my problematic.
In fact I have 2 data frame:
import pandas
blast=pandas.read_table("blast")
cluster=pandas.read_table("cluster")
Here is an exemple of their contents:
>>> cluster
cluster_name seq_names
0 1 g1.t1_0035
1 1 g1.t1_0035_0042
2 119365 g1.t1_0042
3 90273 g1.t1_0042_0035
4 71567 g10.t1_0035
5 37976 g10.t1_0035_0042
6 22560 g10.t1_0042
7 90280 g10.t1_0042_0035
8 82698 g100.t1_0035
9 47392 g100.t1_0035_0042
10 28484 g100.t1_0042
11 22580 g100.t1_0042_0035
12 19474 g1000.t1_0035
13 5770 g1000.t1_0035_0042
14 29708 g1000.t1_0042
15 99776 g1000.t1_0042_0035
16 6283 g10000.t1_0035
17 39828 g10000.t1_0035_0042
18 25383 g10000.t1_0042
19 106614 g10000.t1_0042_0035
20 6285 g10001.t1_0035
21 13866 g10001.t1_0035_0042
22 121157 g10001.t1_0042
23 106615 g10001.t1_0042_0035
24 6286 g10002.t1_0035
25 113 g10002.t1_0035_0042
26 25397 g10002.t1_0042
27 106616 g10002.t1_0042_0035
28 4643 g10003.t1_0035
29 13868 g10003.t1_0035_0042
... ... ...
and
[78793 rows x 2 columns]
>>> blast
qseqid sseqid pident length mismatch \
0 g1.t1_0035_0042 g1.t1_0035_0042 100.0 286 0
1 g1.t1_0035_0042 g1.t1_0035 100.0 257 0
2 g1.t1_0035_0042 g9307.t1_0035 26.9 134 65
3 g2.t1_0035_0042 g2.t1_0035_0042 100.0 445 0
4 g2.t1_0035_0042 g2.t1_0035 95.8 451 3
5 g2.t1_0035_0042 g24520.t1_0042_0035 61.1 429 137
6 g2.t1_0035_0042 g9924.t1_0042 61.1 429 137
7 g2.t1_0035_0042 g1838.t1_0035 86.2 29 4
8 g3.t1_0035_0042 g3.t1_0035_0042 100.0 719 0
9 g3.t1_0035_0042 g3.t1_0035 84.7 753 62
10 g4.t1_0035_0042 g4.t1_0035_0042 100.0 242 0
11 g4.t1_0035_0042 g3.t1_0035 98.8 161 2
12 g5.t1_0035_0042 g5.t1_0035_0042 100.0 291 0
13 g5.t1_0035_0042 g3.t1_0035 93.1 291 0
14 g6.t1_0035_0042 g6.t1_0035_0042 100.0 152 0
15 g6.t1_0035_0042 g4.t1_0035 100.0 152 0
16 g7.t1_0035_0042 g7.t1_0035_0042 100.0 216 0
17 g7.t1_0035_0042 g5.t1_0035 98.1 160 3
18 g7.t1_0035_0042 g11143.t1_0042 46.5 230 99
19 g7.t1_0035_0042 g27537.t1_0042_0035 40.8 233 111
20 g3778.t1_0035_0042 g3778.t1_0035_0042 100.0 86 0
21 g3778.t1_0035_0042 g6174.t1_0035 98.0 51 1
22 g3778.t1_0035_0042 g20037.t1_0035_0042 100.0 50 0
23 g3778.t1_0035_0042 g37190.t1_0035 100.0 50 0
24 g3778.t1_0035_0042 g15112.t1_0042_0035 66.0 53 18
25 g3778.t1_0035_0042 g6061.t1_0042 66.0 53 18
26 g18109.t1_0035_0042 g18109.t1_0035_0042 100.0 86 0
27 g18109.t1_0035_0042 g33071.t1_0035 100.0 81 0
28 g18109.t1_0035_0042 g32810.t1_0035 96.4 83 3
29 g18109.t1_0035_0042 g17982.t1_0035_0042 98.6 72 1
... ... ... ... ... ...
if you stay focus on the cluster database, the first column correspond to the cluster ID and inside those clusters there are several sequences ID.
What I need to to is first to split all my cluster (in R it would be like: liste=split(x = data$V2, f = data$V1) )
And then, creat a function which displays the most similarity paires sequence within each cluster.
here is an exemple:
let's say I have two clusters (dataframe cluster):
cluster 1:
seq1
seq2
seq3
seq4
cluster 2:
seq5
seq6
seq7
...
On the blast dataframe there is on the 3th column the similarity between all sequences (all against all), so something like:
seq1 vs seq1 100
seq1 vs seq2 90
seq1 vs seq3 56
seq1 vs seq4 49
seq1 vs seq5 40
....
seq2 vs seq3 70
seq2 vs seq4 98
...
seq5 vs seq5 100
seq5 vs seq6 89
seq5 vs seq7 60
seq7 vs seq7 46
seq7 vs seq7 100
seq6 vs seq6 100
and what I need to get is :
cluster 1 (best paired sequences):
seq 1 vs seq 2
cluster2 (best paired sequences):
seq 5 vs seq6
...
So as you can see, I do not want to take into account the sequences paired by themselves
IF someone could give me some clues it would be fantastic.
Thank you all.
Firstly I assume that there are no Pairings in 'blast' with sequences from two different Clusters. In other words: in this solution the cluster-ID of a pairing will be evaluated by only one of the two sequence IDs.
Including cluster information and pairing information into one dataframe:
data = cluster.merge(blast, left_on='seq_names', right_on='qseqid')
Then the data should only contain pairings of different sequences:
data = data[data['qseqid']!=data['sseqid']]
To ignore pairings which have the same substrings in their seqid, the most readable way would be to add data columns with these data:
data['qspec'] = [seqid.split('_')[1] for seqid in data['qseqid'].values]
data['sspec'] = [seqid.split('_')[1] for seqid in data['sseqid'].values]
Now equal spec-values can be filtered the same way like it was done with equal seqids above:
data = data[data['qspec']!=data['sspec']]
In the end the data should be grouped by cluster-ID and within each group, the maximum of pident is of interest:
data_grpd = data.groupby('cluster_name')
result = data.loc[data_grpd['pident'].idxmax()]
The only drawback here - except the above mentioned assumption - is, that if there are several exactly equal max-values, only one of them would be taken into account.
Note: if you don't want the spec-columns to be of type string, you could easiliy turn them into integers on the fly by:
import numpy as np
data['qspec'] = [np.int(seqid.split('_')[1]) for seqid in data['qseqid'].values]
This merges the dataframes based first on sseqid, then on qseqid, and then returns results_df. Any with 100% match are filtered out. Let me know if this works. You can then order by cluster name.
blast = blast.loc[blast['pident'] != 100]
results_df = cluster.merge(blast, left_on='seq_names',right_on='sseqid')
results_df = results_df.append(cluster.merge(blast, left_on='seq_names',right_on='qseqid'))