使用字符串使用“ for”语句压缩重复代码-Python

Question

I am very new with "for" statements in Python, and I can't get something that I think should be simple to work. My code that I have is:

import pandas as pd

df1 = pd.DataFrame({'Column1' : pd.Series([1,2,3,4,5,6])})
df2 = pd.DataFrame({'Column1' : pd.Series([1,2,3,4,5,6])})
df3 = pd.DataFrame({'Column1' : pd.Series([1,2,3,4,5,6])})

DF1 = pd.DataFrame({'Column1' : pd.Series([1,2,3,4,5,6])})
DF2 = pd.DataFrame({'Column1' : pd.Series([1,2,3,4,5,6])})
DF3 = pd.DataFrame({'Column1' : pd.Series([1,2,3,4,5,6])})

Then:

A1 = len(df1.loc[df1['Column1'] <= DF1['Column1'].iloc[2]])  
Z1 = len(df1.loc[df1['Column1'] >= DF1['Column1'].iloc[3]])

A2 = len(df2.loc[df2['Column1'] <= DF2['Column1'].iloc[2]])  
Z2 = len(df2.loc[df2['Column1'] >= DF2['Column1'].iloc[3]])

A3 = len(df3.loc[df3['Column1'] <= DF3['Column1'].iloc[2]])  
Z3 = len(df3.loc[df3['Column1'] >= DF3['Column1'].iloc[3]])

As you can see, it is a lot of repeat code with just the identifying numbers being different. So my first attempt at a "for" statement was:

Numbers = [1,2,3]

for i in Numbers:
    "A" + str(i) = len("df" + str(i).loc["df" + str(i)['Column1'] <= "DF" + str(i)['Column1'].iloc[2]])
    "Z" + str(i) = len("df" + str(i).loc["df" + str(i)['Column1'] >= "DF" + str(i)['Column1'].iloc[3]])

This yielded the SyntaxError: "can't assign to operator". So I tried:

Numbers = [1,2,3]

for i in Numbers:
    A = "A" + str(i)
    Z = "Z" + str(i)
    A = len("df" + str(i).loc["df" + str(i)['Column1'] <= "DF" + str(i)['Column1'].iloc[2]])
    Z = len("df" + str(i).loc["df" + str(i)['Column1'] >= "DF" + str(i)['Column1'].iloc[3]])

This yielded the AttributeError: 'str' object has no attribute 'loc'. I tried a few other things like:

Numbers = [1,2,3]

for i in Numbers:
    A = "A" + str(i)
    Z = "Z" + str(i)
    df = "df" + str(i)
    DF = "DF" + str(i)
    A = len(df.loc[df['Column1'] <= DF['Column1'].iloc[2]])
    Z = len(df.loc[df['Column1'] <= DF['Column1'].iloc[3]])

But that just gives me the same errors. Ultimately what I would want is something like:

Numbers = [1,2,3]

for i in Numbers:
     Ai = len(dfi.loc[dfi['Column1'] <= DFi['Column1'].iloc[2]])
     Zi = len(dfi.loc[dfi['Column1'] <= DFi['Column1'].iloc[3]])

Where the output would be equivalent if I typed:

A1 = len(df1.loc[df1['Column1'] <= DF1['Column1'].iloc[2]])  
Z1 = len(df1.loc[df1['Column1'] >= DF1['Column1'].iloc[3]])

A2 = len(df2.loc[df1['Column1'] <= DF2['Column1'].iloc[2]])  
Z2 = len(df2.loc[df1['Column1'] >= DF2['Column1'].iloc[3]])

A3 = len(df3.loc[df3['Column1'] <= DF3['Column1'].iloc[2]])  
Z3 = len(df3.loc[df3['Column1'] >= DF3['Column1'].iloc[3]])

Answer 1

It is "restricted" to generate variables in for loop (you can do that, but it's better to avoid. See other posts: post_1 , post_2 ).

Instead use this code to achieve your goal without generating as many variables as your needs (actually generate only the values in the for loop):

# Lists of your dataframes
Hanimals = [H26, H45, H46, H47, H51, H58, H64, H65]
Ianimals = [I26, I45, I46, I47, I51, I58, I64, I65]

# Generate your series using for loops iterating through your lists above
BPM = pd.DataFrame({'BPM_Base':pd.Series([i_a for i_a in [len(i_h.loc[i_h['EKG-evt'] <=\
     i_i[0].iloc[0]]) / 10 for i_h, i_i in zip(Hanimals, Ianimals)]]),
    'BPM_Test':pd.Series([i_z for i_z in [len(i_h.loc[i_h['EKG-evt'] >=\
     i_i[0].iloc[-1]]) / 30 for i_h, i_i in zip(Hanimals, Ianimals)]])})

UPDATE

A more efficient way (iterate over "animals" lists only once):

# Lists of your dataframes
Hanimals = [H26, H45, H46, H47, H51, H58, H64, H65]
Ianimals = [I26, I45, I46, I47, I51, I58, I64, I65]

# You don't need using pd.Series(),
# just create a list of tuples: [(A26, Z26), (A45, Z45)...] and iterate over it
BPM = pd.DataFrame({'BPM_Base':i[0], 'BPM_Test':i[1]} for i in \
    [(len(i_h.loc[i_h['EKG-evt'] <= i_i[0].iloc[0]]) / 10,
    len(i_h.loc[i_h['EKG-evt'] >= i_i[0].iloc[-1]]) / 30) \
    for i_h, i_i in zip(Hanimals, Ianimals)])

Answer 2

Figured out a better way to do this that fits my needs. This is mainly so that I will be able to find my method.

# Change/Add animals and conditions here, make sure they match up directly

Animal = ['26','45','46','47','51','58','64','65', '69','72','84']
Cond = ['Stomach','Intestine','Stomach','Stomach','Intestine','Intestine','Intestine','Stomach','Cut','Cut','Cut']    

d = []

def CuSO4():
    for i in Animal:

        # load in Spike data
        A = pd.read_csv('TXT/INJ/' + i + '.txt',delimiter=r"\s+", skiprows = 15, header = None, usecols = range(1))
        B = pd.read_csv('TXT/EKG/' + i + '.txt', skiprows = 3)
        C = pd.read_csv('TXT/ESO/' + i + '.txt', skiprows = 3) 
        D = pd.read_csv('TXT/TRACH/' + i + '.txt', skiprows = 3)
        E = pd.read_csv('TXT/BP/' + i + '.txt', delimiter=r"\s+").rename(columns={"4 BP": "BP"})


        # Count number of beats before/after injection, divide by 10/30 minutes for average BPM.
        F = len(B.loc[B['EKG-evt'] <= A[0].iloc[0]])/10   
        G = len(B.loc[B['EKG-evt'] >= A[0].iloc[-1]])/30


        # Count number of esophogeal events before/after injection
        H = len(C.loc[C['Eso-evt'] <= A[0].iloc[0]])
        I = len(C.loc[C['Eso-evt'] >= A[0].iloc[-1]])

        # Find Trach events after injection
        J = D.loc[D['Trach-evt'] >= A[0].iloc[-1]]

        # Count number of breaths before/after injection, divide by 10/30 min for average breaths/min
        K = len(D.loc[D['Trach-evt'] <= A[0].iloc[0]])/10
        L = len(J)/30

        # Use Trach events from J to find the number of EE
        M = pd.DataFrame(pybursts.kleinberg(J['Trach-evt'], s=4, gamma=0.1))
        N = M.last_valid_index()

        # Use N and M to determine the latency, set value to MaxTime (1800s)if EE = 0
        O = 1800 if N == 0 else M.iloc[1][1] - A[0].iloc[-1]

        # Find BP value before/after injection, then determine the mean value
        P = E.loc[E['Time'] <= A[0].iloc[0]]
        Q = E.loc[E['Time'] >= A[0].iloc[-1]]
        R = P["BP"].mean()
        S = Q["BP"].mean()

        # Combine all factors into one DF
        d.append({'EE' : N, 'EE-lat' : O,
          'BPM_Base' : F, 'BPM_Test' : G,
          'Eso_Base' : H, 'Eso_Test' : I,
          'Trach_Base' : K, 'Trach_Test' : L,
          'BP_Base' : R, 'BP_Test' : S})

CuSO4()

# Create shell DF with animal numbers and their conditions.

DF = pd.DataFrame({'Animal' : pd.Series(Animal), 'Cond' : pd.Series(Cond)})

# Pull appended DF from CuSO4 and make it a pd.DF
Df = pd.DataFrame(d)

# Combine the two DF's
df = pd.concat([DF, Df], axis=1)
df