I am working on a data set that contains longitude and latitude values.
I converted those values to clusters using DBSCAN.
Then I plotted the clusters just as a sanity check.
I get this:
The point at (0, 0) is obviously an issue.
So I ran this code to capture which row(s) are a problem.
a = df3.loc[(df3['latitude'] < 0.01) & (df3['longitude'] < 0.01)].index
print(a) # 1812 rows with 0.0 longitude and -2e-08 latitude
I have 1812 rows with missing data all represented as 0.0 longitude and -2e-08 latitude in the source file.
I am debating some imputation strategies but first I want to replace the 0.0 and -2e-08 values
with np.NA or np.nan so that I can then use fillna() with whatever I ultimately decide to do.
I have tried both:
df3.replace((df3['longitude'] == 0.0), pd.NA, inplace=True)
df3.replace((df3['latitude'] == -2e-08), pd.NA, inplace=True)
print(df3['longitude'].value_counts(dropna=False), '\n')
print(df3['latitude'].value_counts(dropna=False))
and
df3.replace((df3['longitude'] < 0.01), pd.NA, inplace=True)
df3.replace((df3['latitude'] < 0.01), pd.NA, inplace=True)
print(df3['longitude'].value_counts(dropna=False), '\n')
print(df3['latitude'].value_counts(dropna=False))
In both cases the existing values remain in place, i.e., the desired substitution with pd.NA
is not occurring.
What would be the correct procedure to replace the unwanted 1812 values in both the latitude and longitude columns with pd.NA or np.nan, as I simply plan to the impute something to replace the null values.
Try this one out:
df3['longitude'] = df3['longitude'].apply(lambda x:np.nan if x == 0.0 else x)
df3['latitude'] = df3['latitude'].apply(lambda x:np.nan if x==-2e-08 else x)
print(df3['longitude'].value_counts(dropna=False), '\n')
print(df3['latitude'].value_counts(dropna=False))
With an example
import numpy as np
import pandas as pd
a = [1, 2, 0.0, -2e-08]
b = [1, 2, 0.0, -2e-08]
df = pd.DataFrame(zip(a, b))
df.columns = ['lat', 'long']
df.long = df.long.apply(lambda x:np.nan if x == 0.0 else x)
df.lat = df.lat.apply(lambda x:np.nan if x==-2e-08 else x)
I have a dataframe and the function that I would like to apply:
>>> import pandas as pd
>>> import numpy as np
>>> df = pd.DataFrame({
... 'A' : ['A1', 'A2', 'A3'],
... 'B' : ['B1', 'B2', 'B3'],
... 'format_str' : [None, np.nan, 'A = {A}, B = {B}']
... }
... )
>>> df
A B format_str
0 A1 B1 None
1 A2 B2 NaN
2 A3 B3 A = {A}, B = {B}
>>> def gen_format_str(ser):
... if pd.isna(ser.format_str):
... return ser.A
... else:
... # return ser.format_str.format(A = ser.A, B=ser.B)
... return ser.format_str.format(**ser)
...
>>> df['new_field'] = df.apply(
... gen_format_str, axis=1
... )
>>> df
A B format_str new_field
0 A1 B1 None A1
1 A2 B2 NaN A2
2 A3 B3 A = {A}, B = {B} A = A3, B = B3
>>>
Everything works as it should, but I would like to use lambda function instead of gen_format_str.
I tried different approaches, but none of them worked.
How to implement the same functionality of gen_format_str by using lambda function in apply method?
Regards.
This seems to be doing the job :
df['new_field'] = df.apply(
lambda ser: ser.A if pd.isna(ser.format_str) else ser.format_str.format(**ser),
axis=1
)
I am having the below dataframe and would like to calculate the difference between columns 'animal1' and 'animal2' over their sum within a function while only taking into consideration the values that are bigger than 0 in each of the columns 'animal1' and 'animal2.
How could I do this?
import pandas as pd
animal1 = pd.Series({'Cat': 4, 'Dog': 0,'Mouse': 2, 'Cow': 0,'Chicken': 3})
animal2 = pd.Series({'Cat': 2, 'Dog': 3,'Mouse': 0, 'Cow': 1,'Chicken': 2})
data = pd.DataFrame({'animal1':animal1, 'animal2':animal2})
def animals():
data['anim_diff']=(data['animal1']-data['animal2'])/(data['animal1']+ ['animal2'])
return data['anim_diff'].abs().idxmax()
print(data)
I believe you need check all rows are greater by 0 with DataFrame.gt with test DataFrame.all and filter by boolean indexing:
def animals(data):
data['anim_diff']=(data['animal1']-data['animal2'])/(data['animal1']+ data['animal2'])
return data['anim_diff'].abs().idxmax()
df = data[data.gt(0).all(axis=1)].copy()
#alternative for not equal 0
#df = data[data.ne(0).all(axis=1)].copy()
print (df)
animal1 animal2
Cat 4 2
Chicken 3 2
print(animals(df))
Cat
i have this problem to solve, this is a continuation of a previus question How to iterate over pandas df with a def function variable function and the given answer worked perfectly, but now i have to append all the data in a 2 columns dataframe (Adduct_name and mass).
This is from the previous question:
My goal: i have to calculate the "adducts" for a given "Compound", both represents numbes, but for eah "Compound" there are 46 different "Adducts".
Each adduct is calculated as follow:
Adduct 1 = [Exact_mass*M/Charge + Adduct_mass]
where exact_mass = number, M and Charge = number (1, 2, 3, etc) according to each type of adduct, Adduct_mass = number (positive or negative) according to each adduct.
My data: 2 data frames. One with the Adducts names, M, Charge, Adduct_mass. The other one correspond to the Compound_name and Exact_mass of the Compounds i want to iterate over (i just put a small data set)
Adducts: df_al
import pandas as pd
data = [["M+3H", 3, 1, 1.007276], ["M+3Na", 3, 1, 22.989], ["M+H", 1, 1,
1.007276], ["2M+H", 1, 2, 1.007276], ["M-3H", 3, 1, -1.007276]]
df_al = pd.DataFrame(data, columns=["Ion_name", "Charge", "M", "Adduct_mass"])
Compounds: df
import pandas as pd
data1 = [[1, "C3H64O7", 596.465179], [2, "C30H42O7", 514.293038], [4,
"C44H56O8", 712.397498], [4, "C24H32O6S", 448.191949], [5, "C20H28O3",
316.203834]]
df = pd.DataFrame(data1, columns=["CdId", "Formula", "exact_mass"])
The solution to this problem was:
df_name = df_al["Ion_name"]
df_mass = df_al["Adduct_mass"]
df_div = df_al["Charge"]
df_M = df_al["M"]
#Defining general function
def Adduct(x,i):
return x*df_M[i]/df_div[i] + df_mass[i]
#Applying general function in a range from 0 to 5.
for i in range(5):
df[df_name.loc[i]] = df['exact_mass'].map(lambda x: Adduct(x,i))
Output
Name exact_mass M+3H M+3Na M+H 2M+H M-3H
0 a 596.465179 199.829002 221.810726 597.472455 1193.937634 197.814450
1 b 514.293038 172.438289 194.420013 515.300314 1029.593352 170.423737
2 c 712.397498 238.473109 260.454833 713.404774 1425.802272 236.458557
3 d 448.191949 150.404592 172.386316 449.199225 897.391174 148.390040
4 e 316.203834 106.408554 128.390278 317.211110 633.414944 104.39400
Now that is the rigth calculations but i need now a file where:
-only exists 2 columns (Name and mass)
-All the different adducts are appended one after another
desired out put
Name Mass
a_M+3H 199.82902
a_M+3Na 221.810726
a_M+H 597.472455
a_2M+H 1193.937634
a_M-3H 197.814450
b_M+3H 514.293038
.
.
.
c_M+3H
and so on.
Also i need to combine the name of the respective compound with the ion form (M+3H, M+H, etc).
At this point i have no code for that.
I would apprecitate any advice and a better approach since the begining.
This part is an update of the question above:
Is posible to obtain and ouput like this one:
Name Mass RT
a_M+3H 199.82902 1
a_M+3Na 221.810726 1
a_M+H 597.472455 1
a_2M+H 1193.937634 1
a_M-3H 197.814450 1
b_M+3H 514.293038 3
.
.
.
c_M+3H 2
The RT is the same value for all forms of a compound, in this example is RT for a =1, b = 3, c =2, etc.
Is posible to incorporate (Keep this column) from the data set df (which i update here below)?. As you can see that df has more columns like "Formula" and "RT" which desapear after calculations.
import pandas as pd
data1 = [[a, "C3H64O7", 596.465179, 1], [b, "C30H42O7", 514.293038, 3], [c,
"C44H56O8", 712.397498, 2], [d, "C24H32O6S", 448.191949, 4], [e, "C20H28O3",
316.203834, 1.5]]
df = pd.DataFrame(data1, columns=["Name", "Formula", "exact_mass", "RT"])
Part three! (sorry and thank you)
this is a trial i did on a small data set (df) using the code below, with the same df_al of above.
df=
Code
#Defining variables for calculation
df_name = df_al["Ion_name"]
df_mass = df_al["Adduct_mass"]
df_div = df_al["Charge"]
df_M = df_al["M"]
df_ID= df["Name"]
#Defining the RT dictionary
RT = dict(zip(df["Name"], df["RT"]))
#Removing RT column
df=df.drop(columns=["RT"])
#Defining general function
def Adduct(x,i):
return x*df_M[i]/df_div[i] + df_mass[i]
#Applying general function in a range from 0 to 46.
for i in range(47):
df[df_name.loc[i]] = df['exact_mass'].map(lambda x: Adduct(x,i))
df
output
#Melting
df = pd.melt(df, id_vars=['Name'], var_name = "Adduct", value_name= "Exact_mass", value_vars=[x for x in df.columns if 'Name' not in x and 'exact' not in x])
df['name'] = df.apply(lambda x:x[0] + "_" + x[1], axis=1)
df['RT'] = df.Name.apply(lambda x: RT[x[0]] if x[0] in RT else np.nan)
del df['Name']
del df['Adduct']
df['RT'] = df.name.apply(lambda x: RT[x[0]] if x[0] in RT else np.nan)
df
output
Why NaN?
Here is how I will go about it, pandas.melt comes to rescue:
import pandas as pd
import numpy as np
from io import StringIO
s = StringIO('''
Name exact_mass M+3H M+3Na M+H 2M+H M-3H
0 a 596.465179 199.829002 221.810726 597.472455 1193.937634 197.814450
1 b 514.293038 172.438289 194.420013 515.300314 1029.593352 170.423737
2 c 712.397498 238.473109 260.454833 713.404774 1425.802272 236.458557
3 d 448.191949 150.404592 172.386316 449.199225 897.391174 148.390040
4 e 316.203834 106.408554 128.390278 317.211110 633.414944 104.39400
''')
df = pd.read_csv(s, sep="\s+")
df = pd.melt(df, id_vars=['Name'], value_vars=[x for x in df.columns if 'Name' not in x and 'exact' not in x])
df['name'] = df.apply(lambda x:x[0] + "_" + x[1], axis=1)
del df['Name']
del df['variable']
RT = {'a':1, 'b':2, 'c':3, 'd':5, 'e':1.5}
df['RT'] = df.name.apply(lambda x: RT[x[0]] if x[0] in RT else np.nan)
df
Here is the output:
I have a table with 5 columns: AWA, REM, S1, S2 and SWS. I computed the sum of each column and of each row. And also the percentages of these values. But in order to get the percentages I repeated the same line 5 times.
Is there a way to improve it in case there were more than 5 columns?
Here is my code and I have also attached an image.
import pandas as pd
df = pd.DataFrame(TABLA, columns=('AWA', 'REM', 'S1', 'S2', 'SWS'))
df.index='s' + (df.index+1).astype(str)
df['xSubject'] = df.sum(axis=1) #sums each colummn
######Here starts the repetition:
df['AWA%'] = df['AWA']/df['AWA'].sum()*100
df['REM%'] = df['REM']/df['REM'].sum()*100
df['S1%'] = df['S1']/df['S1'].sum()*100
df['S2%'] = df['S2']/df['S2'].sum()*100
df['SWS%'] = df['SWS']/df['SWS'].sum()*100
df['xSubject%'] = df['xSubject']/df['xSubject'].sum()*100
######Here ends the repetition:
df.loc['xStage'] = df.sum() #sums each row
df
Use pd.concat with a reconstruction
pd.concat([df,
pd.DataFrame(df.div(df.sum()).values * 100,
columns=df.columns.values + '%')],
axis=1)
Consider the pd.DataFrame df
df = pd.DataFrame(np.random.rand(10, 5),
columns=('AWA', 'REM', 'S1', 'S2', 'SWS'))
df
and the % calculation
df.div(df.sum())
Then using the above code
pd.concat([df,
pd.DataFrame(df.div(df.sum()).values * 100,
columns=df.columns.values + '%')],
axis=1)