I have a single column within a dataframe which comprises both the index (virus) and the data to tabulate and wish to convert to wide format.
Input data
virus1
AGCTGAGTGAG # sequence
40.1 # score 1
23 # score 2
102 # score 3
virus2
AGCTGAGTGAG # sequence
43.4 # score 1
32 # score 2
101 # score 3
virus3
AGTTGAGTGAG # sequence
41.3 # score 1
35 # score 2
100 # score 3
.... >100 inputs
Dataframe output
sequence score1 score2 score3
virus1 AGCTGAGTGAG 40.1 43.4 41.3
virus2 AGCTGAGTGAG 23 32 35
virus3 AGTTGAGTGAG 102 101 100
I attempted to import the data into a single dataframe and move the rows into columns of a new dataframe
Code
df = pd.read_csv(file, sep='\n', header=None)
index_labels = df.iloc[::4].astype(str)
dfvirus = pd.DataFrame(index=labels)
dfvirus['sequence'] = df.iloc[1::5].astype(str)
dfvirus['score1'] = df.iloc[2::5].astype(float)
dfvirus['score2'] = df.iloc[3::5].astype(int)
dfvirus['score3'] = df.iloc[4::5].astype(int)
The above didn't work I get NaN or nan for the values of e.g. dfvirus['sequence'].head() depending on whether the input is a number or a string. I could do this by constructing a hierarchical index, but that would mean looping a very long index into a list.
Moving from long to wide format is a common issue and I would be grateful if you could show a simpler solution or where I'm going wrong here.
You can do:
df = pd.read_csv(file, sep='\n', header=None)
new_df = pd.DataFrame(df.values.reshape(-1,5),
columns=['virus','sequence','score1','score2','score3']
)
Output
virus sequence score1 score2 score3
0 virus1 AGCTGAGTGAG 40.1 23 102
1 virus2 AGCTGAGTGAG 43.4 32 101
2 virus3 AGTTGAGTGAG 41.3 35 100
Related
My goal is to aggregate data similar to SAS's "proc summary using types" My starting pandas dataframe could look like this where the database has already done the original group by all dimensions/classification variables and done some aggregate function on the measures.
So in sql this would look like
select gender, age, sum(height), sum(weight)
from db.table
group by gender, age
gender
age
height
weight
F
19
70
123
M
24
72
172
I then would like to summarize the data using pandas to calculate summary rows based on different group bys to come out with this.
gender
age
height
weight
.
.
142
295
.
19
70
123
.
24
72
172
F
.
70
123
M
.
72
172
F
19
70
123
M
24
72
172
Where the first row is agg with no group by
2 and 3 row are agg grouped by age
4 and 5 agg by just gender
and then just the normal rows
My current code looks like this
# normally dynamic just hard coded for this example
measures = {'height':{'stat':'sum'}, 'age':{'stat':'sum'}}
msr_config_dict = {}
for measure in measures:
if measure in message_measures:
stat = measures[measure]['stat']
msr_config_dict[measure] = pd.NamedAgg(measure, stat)
# compute agg with no group by as starting point
df=self.df.agg(**msr_config_dict)
dimensions = ['gender','age'] # also dimensions is dynamic in real life
dim_vars = []
for dim in dimensions:
dim_vars.append(dim)
if len(dim_vars) > 1:
# compute agg of compound dimensions
df_temp = self.df.groupby(dim_vars, as_index=False).agg(msr_config_dict)
df = df.append(df_temp, ignore_index=True)
# always compute agg of solo dimension
df_temp = self.df.groupby(dim, as_index=False).agg(msr_config_dict)
df = df.append(df_temp, ignore_index=True)
With this code I get AttributeError: 'height' is not a valid function for 'Series' object
For the input to agg function I have also tried
{'height':[('height', 'sum')], 'weight':[('weight', 'sum')]} where I am trying to compute the sum of all heights and name the output height. Which also had an attribute error.
I know I will only ever be computing one aggregate function per measure so I would like to dynamically build the input to the pandas agg functon and always rename the stat to itself so I can just append it to the dataframe that I am building with the summary rows.
I am new to pandas coming from SAS background.
Any help would be much appreciated.
IIUC:
cols = ['height', 'weight']
out = pd.concat([df[cols].sum(0).to_frame().T,
df.groupby('age')[cols].sum().reset_index(),
df.groupby('gender')[cols].sum().reset_index(),
df], ignore_index=True)[df.columns].fillna('.')
Output:
>>> out
gender age height weight
0 . . 142 295
1 . 19.0 70 123
2 . 24.0 72 172
3 F . 70 123
4 M . 72 172
5 F 19.0 70 123
6 M 24.0 72 172
Here is a more flexible solution, extending the solution of #Corralien. You can use itertools.combinations to create all the combinations of dimensions and for all length of combination possible.
from itertools import combinations
# your input
measures = {'height':{'stat':'sum'}, 'weight':{'stat':'min'}}
dimensions = ['gender','age']
# change the nested dictionary
msr_config_dict = {key:val['stat'] for key, val in measures.items()}
# concat all possible aggregation
res = pd.concat(
# case with all aggregated
[df.agg(msr_config_dict).to_frame().T]
# cases at least one column to aggregate over
+ [df.groupby(list(_dimCols)).agg(msr_config_dict).reset_index()
# for combinations of length 1, 2.. depending on the number of dimensions
for nb_cols in range(1, len(dimensions))
# all combinations of the specific lenght
for _dimCols in combinations(dimensions, nb_cols)]
# original dataframe
+ [df],
ignore_index=True)[df.columns].fillna('.')
print(res)
# gender age height weight
# 0 . . 142 123
# 1 F . 70 123
# 2 M . 72 172
# 3 . 19.0 70 123
# 4 . 24.0 72 172
# 5 F 19.0 70 123
# 6 M 24.0 72 172
I am trying to determine which sequential rows have at least 50 duplicates within one column. Then I would like to be able to read which rows have the duplicates in a summarized manner, ie
start end total
9 60 51
200 260 60
I'm trying to keep the start and end separate so I can call on them independently later.
I have this to open the .csv file and read its contents:
df = pd.read_csv("BN4 A4-F4, H4_row1_column1_watershed_label.csv", header=None)
df.groupby(0).filter(lambda x: len(x) > 0)
Which gives me this:
0
0 52.0
1 65.0
2 52.0
3 52.0
4 52.0
... ...
4995 8.0
4996 8.0
4997 8.0
4998 8.0
4999 8.0
5000 rows × 1 columns
I'm having a number of problems with this. 1) I'm not sure I totally understand the second function. It seems like it is supposed to group the numbers in my column together. This code:
df.groupby(0).count()
gives me this:
0
0.0
1.0
2.0
3.0
4.0
...
68.0
69.0
70.0
71.0
73.0
65 rows × 0 columns
Which I assume means that there are a total of 65 different unique identities in my column. This just doesn't tell me what they are or where they are. I thought that's what this one would do
df.groupby(0).filter(lambda x: len(x) > 0)
but if I change the 0 to anything else then it screws up my generated list.
Problem 2) I think in order to get the number of duplicates in a sequence, and which rows they are in, I would probably need to use a for loop, but I'm not sure how to build it. So far, I've been pulling my hair out all day trying to figure it out but I just don't think I know Python well enough yet.
Can I get some help, please?
UPDATE
Thanks! So this is what I have thanks to #piterbarg:
#function to identify which behaviors have at least 49 frames, and give the starting, ending, and number of frames
def behavior():
df2 = (df
.reset_index()
.shift(periods=-1)
.groupby((df[0].diff() != 0).cumsum()) #if the diff between a row and the prev row is not 0, increase cumulative sum
.agg({0 : 'mean', 'index':['first','last',len]})) #mean is the behavior category
df3 = (df2.where(df2[('index','len')]>49)
.dropna() #drop N/A
.astype(int) #type = int
.reset_index(drop = True))
print(df3)
out:
0 index
mean first last len
0 7 32 87 56
1 19 277 333 57
2 1 785 940 156
3 30 4062 4125 64
4 29 4214 4269 56
5 7 4450 4599 150
6 1 4612 4775 164
7 7 4778 4882 105
8 8 4945 4999 56
The current issue is trying to make it so the dataframe includes the last row of my .csv. If anyone happens to see this, I would love your input!
Let's start by mocking a df:
import numpy as np
np.random.seed(314)
df=pd.DataFrame({0:np.random.randint(10,size = 5000)})
# make sure we have a couple of large blocks
df.loc[300:400,0] = 5
df.loc[600:660,0] = 4
First we identify where the changes to the consecutive numbers occur, and groupby each of such groups. We record where it starts, where it finishes, and the size of each group
df2 = (df.reset_index()
.groupby((df[0].diff() != 0).cumsum())
.agg({'index':['first','last',len]})
)
Then we only pick those groups that are longer than 50
(df2.where(df2[('index','len')]>50)
.dropna()
.astype(int)
.reset_index(drop = True)
)
output:
index
first last len
0 300 400 101
1 600 660 61
For your question as to what df.groupby(0).filter(lambda x: len(x) > 0) does, as far as I can tell it does nothing. It groups by different values in column 0 and then discard those groups whose size is 0, which is none of them by definition. So this returns your full df
Edit
Your code is not quite right, should be
def behavior():
df2 = (df.reset_index()
.groupby((df[0].diff() != 0).cumsum())
.agg({0 : 'mean', 'index':['first','last',len]}))
df3 = (df2.where(df2[('index','len')]>50)
.dropna()
.astype(int)
.reset_index(drop = True))
print(df3)
note that we define and return df3 not df2, and also I amended the code to return the value that is repeated in the mean column (sorry names are not very intuitive but you can change them if you want)
first is the index when the repetition starts, last is the last index, and len is how many elements there.
#function to identify which behaviors have at least 49 frames, and give the starting, ending, and number of frames
def behavior():
df2 = (df.reset_index()
.groupby((df[0].diff() != 0).cumsum()) #if the diff between a row and the prev row is not 0, increase cumulative sum
.agg({0 : 'mean', 'index':['first','last',len]})) #mean is the behavior category
.shift(-1)
df3 = (df2.where(df2[('index','len')]>49)
.dropna() #drop N/A
.astype(int) #type = int
.reset_index(drop = True))
print(df3)
yields this:
0 index
mean first last len
0 7 31 86 56
1 19 276 332 57
2 1 784 939 156
3 31 4061 4124 64
4 29 4213 4268 56
5 8 4449 4598 150
6 1 4611 4774 164
7 8 4777 4881 105
8 8 4944 4999 56
Which I love. I did notice that the group with 56x duplicates of '7' actually starts on row 32, and ends on row 87 (just one later in both cases, and the pattern is consistent throughout the sheet). Am I right in believing that this can be fixed with the shift() function somehow? I'm toying around with this still :D
I am trying to calculate the similarity between cities in my dataframe, and 1 static city name. (eventually I want to iterate through a dataframe and choose the best matching city name from that data frame, but I am testing my code on this simplified scenario).
I am using fuzzywuzzy token set ratio.
For some reason it calculates the first row correctly, and it seems it assigns the same value for all rows.
code:
from fuzzywuzzy import fuzz
test_df= pd.DataFrame( {"City" : ["Amsterdam","Amsterdam","Rotterdam","Zurich","Vienna","Prague"]})
test_df = test_df.assign(Score = lambda d: fuzz.token_set_ratio("amsterdam",test_df["City"]))
print (test_df.shape)
test_df.head()
Result:
City Score
0 Amsterdam 100
1 Amsterdam 100
2 Rotterdam 100
3 Zurich 100
4 Vienna 100
If I do the comparison one by one it works:
print (fuzz.token_set_ratio("amsterdam","Amsterdam"))
print (fuzz.token_set_ratio("amsterdam","Rotterdam"))
print (fuzz.token_set_ratio("amsterdam","Zurich"))
print (fuzz.token_set_ratio("amsterdam","Vienna"))
Results:
100
67
13
13
Thank you in advance!
I managed to solve it via iterating through the rows:
for index,row in test_df.iterrows():
test_df.loc[index, "Score"] = fuzz.token_set_ratio("amsterdam",test_df.loc[index,"City"])
The result is:
City Country Code Score
0 Amsterdam NL 100
1 Amsterdam NL 100
2 Rotterdam NL 67
3 Zurich NL 13
4 Vienna NL 13
I need to search the values from the df1['numsearch'] column into the lists in df2['Numbers']. If the number is in those lists, then I want to add values from the df2['Score'] column to df1. See desired output below.
df1 = pd.DataFrame(
{'Day':['M','Tu','W','Th','Fr','Sa','Su'],
'numsearch':['1','20','14','99','19','6','101']
})
df2 = pd.DataFrame(
{'Letters':['a','b','c','d'],
'Numbers':[['1','2','3','4'],['5','6','7','8'],['10','20','30','40'],['11','12','13','14']],
'Score': ['1.1','2.2','3.3','4.4']})
desired output
Day numsearch Score
0 M 1 1.1
1 Tu 20 3.3
2 W 4 4.4
3 Th 99 "No score"
4 Fr 19 "No score"
5 Sa 6 2.2
6 Su 101 "No score"
I have written a for loop that works with the test data.
scores = []
for s,ns in enumerate(ppr_data['SN']):
match = ''
for k,q in enumerate(jcr_data['All_ISSNs']):
if ns in q:
scores.append(jcr_data['Journal Impact Factor'][k])
match = 1
else:
continue
if match == "":
scores.append('No score')
match = ""
df1['Score'] = np.array(scores)
In my small test, but above code works, but when working with larger data files, it is creating duplicates. So this clearly isn't the best way to do this.
I'm sure there's a more pandas-proper line of code that ends in .fillna("No score") .
I tried to use a loc statement, but I get hung up on searching the values of one dataframe in a column that contains lists.
Can anyone shed some light?
df2=df2.explode('Numbers')#Explode df2 on Numbers
d=dict(zip(df2.Numbers, df2.Score))#dict Numbers and Scores
df1['Score']=df1.numsearch.map(d).fillna('No Score')#Map dict to df1 filling NaN with No Score
Can shorten it as follows:
df2=df2.explode('Numbers')#Explode df2 on Numbers
df1['Score']=df1.numsearch.map(dict(zip(df2.Numbers, df2.Score))).fillna('No Score')
Day numsearch Score
0 M 1 1.1
1 Tu 20 3.3
2 W 14 4.4
3 Th 99 No Score
4 Fr 19 No Score
5 Sa 6 2.2
6 Su 101 No Score
You can try left join and fillna:
df1.merge(df2.explode('Numbers'),
left_on='numsearch',
right_on='Numbers', how='left')[['Day', 'numsearch', 'Score']].fillna("No score")
Output:
Day numsearch Score
0 M 1 1.1
1 Tu 20 3.3
2 W 14 4.4
3 Th 99 No score
4 Fr 19 No score
5 Sa 6 2.2
6 Su 101 No score
My current DataFrame is:
Term value
Name
A 1 35
A 2 40
A 3 50
B 1 20
B 2 45
B 3 50
I want to get a dataframe as:
Term 1 2 3
Name
A 35 40 50
B 20 45 50
How can i get it?I've tried using pivot_table but i didn't get my expected output.Is there any way to get my expected output?
Use:
df = df.set_index('Term', append=True)['value'].unstack()
Or:
df = pd.pivot(df.index, df['Term'], df['value'])
print (df)
Term 1 2 3
Name
A 35 40 50
B 20 45 50
EDIT: If duplicates in pairs Name with Term is necessary aggretion, e.g. sum or mean:
df = df.groupby(['Name','Term'])['value'].sum().unstack(fill_value=0)