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
Related
I have a pandas dataframe with the following form:
cluster number
Robin_lodging_Dorthy 0
Robin_lodging_Phillip 1
Robin_lodging_Elmer 2
... ...
I want to replace replace every 0 that is in the column cluster number with with the string "low", every 1 with "mid" and every 2 with "high". Any idea of how that can be possible?
You can use replace function with some mappings to change your column values:
values = {
0: 'low',
1: 'mid',
2: 'high'
}
data = {
'name': ['Robin_lodging_Dorthy', 'Robin_lodging_Phillip', 'Robin_lodging_Elmer'],
'cluster_number': [0, 1, 2]
}
df = pd.DataFrame(data)
df.replace({'cluster_number': values}, inplace=True)
df
Output:
name cluster_number
0 Robin_lodging_Dorthy low
1 Robin_lodging_Phillip mid
2 Robin_lodging_Elmer high
More info on replace function.
I have dataframe with 7 string columns:
bul; age; gender; hh; pn; freq_pn; rcrds_to_select
1; 2; 5; 1; ['35784905', '40666303', '47603805', '68229102'];4;3
2; 3; 3; 3; ['06299501', '07694901', '35070201'];3;2
In the last column I have the number of id's from "pn" column that I need to select randomly. Example: in the first row I have 4 id's ['35784905', '40666303', '47603805', '68229102'] and I need to select 3 random id's and remove the not selected one. There can be rows with only one id. I came to the conclusion that I need to turn the values in tuples and store them in another column ('pnTuple'). I don't know if this is the right way.
mass_grouped3['pnTuple'] = [tuple(x) for x in mass_grouped3['pn'].values]
I think random.shuffle will do the job, but have no idea how to implement it in my script. I was thinking something like this, but is not working:
for row in mass_grouped3['pnTuple']:
list = list(mass_grouped3['pnTuple'])
whitelist = random.shuffle(list)
Any ideas how to do this selection are appreciated.
You want to randomly select 1 from every row and make the rest 0. Here's one approach. Sample the indices and based on indices assign 1. i.e
idx = pd.DataFrame(np.stack(np.where(df==1))).T.groupby(0).apply(lambda x: x.sample(1)).values
# array([[0, 2],
# [1, 1],
# [2, 0],
# [3, 3]])
ndf = pd.DataFrame(np.zeros(df.shape),columns=df.columns)
ndf.values[idx[:,0],idx[:,1]] = 1
W1 W2 W3 W4
0 0 0 1 0
1 1 0 0 0
2 1 0 0 0
3 0 1 0 0
Welcome to StackOverflow! Hope this helps
Lets go step by step
First lets construct our random function that can select 3
>>> import random
>>> random.choices(['35784905', '40666303', '47603805', '68229102'], k=3)
['68229102', '40666303', '35784905']
I have a sample data frame, df with columns with same data as yours
>>> df
a b
0 12 [35784905, 40666303, 47603805, 68229102]
1 12 [06299501, 07694901, 35070201]
>>> df['b']
0 [35784905, 40666303, 47603805, 68229102]
1 [06299501, 07694901, 35070201]
Name: b, dtype: object
>>> df['b'].map(lambda alist: random.choices(alist, k=3) if len(alist) > 3 else alist)
0 [35784905, 68229102, 35784905]
1 [06299501, 07694901, 35070201]
Name: b, dtype: object
>>> df['b'] = df['b'].map(lambda alist: random.choices(alist, k=3) if len(alist) > 3 else alist)
Using pandas map operation to apply this data transformation to whole columns
Note: We are using a lambda function lambda alist: random.choices(alist, k=3) if len(alist) > 3 else alist to ensure that each list has more than 3 items, and only then apply this operation.
It might be a little new, but this a standard way of writing code in python. Learn more about Python, lambda function and pandas for some time.
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'm trying to write a code that takes analyses values in a dataframe, if the values fall in a class, the total number of those values are assigned to a key in the dictionary. But the code is not working for me. Im trying to create logarithmic classes and count the total number of values that fall in it
def bins(df):
"""Returns new df with values assigned to bins"""
bins_dict = {500: 0, 5000: 0, 50000: 0, 500000: 0}
for i in df:
if 100<i and i<=1000:
bins_dict[500]+=1,
elif 1000<i and i<=10000:
bins_dict[5000]+=1
print(bins_dict)
However, this is returning the original dictionary.
I've also tried modifying the dataframe using
def transform(df, range):
for i in df:
for j in range:
b=10**j
while j==1:
while i>100:
if i>=b:
j+=1,
elif i<b:
b = b/2,
print (i = b*(int(i/b)))
This code is returning the original dataframe.
My dataframe consists of only one column with values ranging between 100 and 10000000
Data Sample:
Area
0 1815
1 907
2 1815
3 907
4 907
Expected output
dict={500:3, 5000:2, 50000:0}
If i can get a dataframe output directly that would be helpful too
PS. I am very new to programming and I only know python
You need to use pandas for it:
import pandas as pd
df = pd.DataFrame()
df['Area'] = [1815, 907, 1815, 907, 907]
# create new column to categorize your data
df['bins'] = pd.cut(df['Area'], [0,1000,10000,100000], labels=['500', '5000', '50000'])
# converting into dictionary
dic = dict(df['bins'].value_counts())
print(dic)
Output:
{'500': 3, '5000': 2, '50000': 0}
I'm trying to loop through a list(y) and output by appending a row for each item to a dataframe.
y=[datetime.datetime(2017, 3, 29), datetime.datetime(2017, 3, 30), datetime.datetime(2017, 3, 31)]
Desired Output:
Index Mean Last
2017-03-29 1.5 .76
2017-03-30 2.3 .4
2017-03-31 1.2 1
Here is the first and last part of the code I currently have:
import pandas as pd
import datetime
df5=pd.DataFrame(columns=['Mean','Last'],index=index)
for item0 in y:
.........
.........
df=df.rename(columns = {0:'Mean'})
df4=pd.concat([df, df3], axis=1)
print (df4)
df5.append(df4)
print (df5)
My code only puts one row into the dataframe like as opposed to a row for each item in y:
Index Mean Last
2017-03-29 1.5 .76
Try:
y = [datetime(2017, 3, 29), datetime(2017, 3, 30),datetime(2017, 3, 31)]
m = [1.5,2.3,1.2]
l = [0.76, .4, 1]
df = pd.DataFrame([],columns=['time','mean','last'])
for y0, m0, l0 in zip(y,m,l):
data = {'time':y0,'mean':m0,'last':l0}
df = df.append(data, ignore_index=True)
and if you want y to be the index:
df.index = df.time
There are a few ways to skin this, and it's hard to know which approach makes the most sense with the limited info given. But one way is to start with a dataframe that has only the index, iterate through the dataframe by row and populate the values from some other process. Here's an example of that approach:
import datetime
import numpy as np
import pandas as pd
y=[datetime.datetime(2017, 3, 29), datetime.datetime(2017, 3, 30), datetime.datetime(2017, 3, 31)]
main_df = pd.DataFrame(y, columns=['Index'])
#pop in the additional columns you want, but leave them blank
main_df['Mean'] = None
main_df['Last'] = None
#set the index
main_df.set_index(['Index'], inplace=True)
that gives us the following:
Mean Last
Index
2017-03-29 None None
2017-03-30 None None
2017-03-31 None None
Now let's loop and plug in some made up random values:
## loop through main_df and add values
for (index, row) in main_df.iterrows():
main_df.ix[index].Mean = np.random.rand()
main_df.ix[index].Last = np.random.rand()
this results in the following dataframe which has the None values filled:
Mean Last
Index
2017-03-29 0.174714 0.718738
2017-03-30 0.983188 0.648549
2017-03-31 0.07809 0.47031