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how slice by hybrid stile

having a random df
df = pd.DataFrame([[1,2,3,4],[4,5,6,7],[7,8,9,10],[10,11,12,13],[14,15,16,17]], columns=['A', 'B','C','D'])
cols_in = list(df)[0:2]+list(df)[4:]
now:
x = []
for i in range(df.shape[0]):
x.append(df.iloc[i,cols_in])
obviously in the cycle, x return an error due to col_in assignment in iloc.
How could be possible apply a mixed style slicing of df like in append function ?

It seems like you want to exclude one column? There is no column 4, so depending on which columns you are after, something like this might be what you are after:
df = pd.DataFrame([[1,2,3,4],[4,5,6,7],[7,8,9,10],[10,11,12,13],[14,15,16,17]], columns=['A', 'B','C','D'])
If you want to get the column indeces from column names you can do:
cols = ['A', 'B', 'D']
cols_in = np.nonzero(df.columns.isin(cols))[0]
x = []
for i in range(df.shape[0]):
x.append(df.iloc[i, cols_in].to_list())
x
Output:
[[1, 2, 4], [4, 5, 7], [7, 8, 10], [10, 11, 13], [14, 15, 17]]

Specify columns to output with Pandas Merge function

import pandas as pd
df1 = pd.DataFrame({'lkey': ['foo', 'bar', 'baz', 'foo'],
'value': [1, 2, 3, 5]})
df2 = pd.DataFrame({'rkey': ['dog', 'bar', 'baz', 'foo'],
'value': [5, 6, 7, 8],
'valuea': [9, 10, 11, 12],
'valueb': [13, 14, 15, 16]})
I would like to merge these 2 dataframes based on 'value'. However I don't want the result to give me all of the columns in df2. I would like to keep the one with the 'valuea' column header but not the one with 'valueb' column header as per the squared output in the image.
The code I have tried is
df1.merge(df2, on ='value')
Is there a way exclude column with header = valueb using parameters in the merge function?

You cannot exclude columns with a parameter in the merge function.
Try these approaches instead:
pd.merge(df1, df2).drop(columns=['valueb'])
pd.merge(df1, df2.drop(columns=['valueb']))

Filling column of dataframe based on 'groups' of values of another column

I am trying to fill values of a column based on the value of another column. Suppose I have the following dataframe:
import pandas as pd
data = {'A': [4, 4, 5, 6],
'B': ['a', np.nan, np.nan, 'd']}
df = pd.DataFrame(data)
And I would like to fill column B but only if the value of column A equals 4. Hence, all rows that have the same value as another in column A should have the same value in column B (by filling this).
Thus, the desired output should be:
data = {'A': [4, 4, 5, 6],
'B': ['a', a, np.nan, 'd']}
df = pd.DataFrame(data)
I am aware of the fillna method, but this gives the wrong output as the third row also gets the value 'A' assigned:
df['B'] = fillna(method="ffill", inplace=True)
data = {'A': [4, 4, 5, 6],
'B': ['a', 'a', 'a', 'd']}
df = pd.DataFrame(data)
How can I get the desired output?

Try this:
df['B'] = df.groupby('A')['B'].ffill()
Output:
>>> df
A B
0 4 a
1 4 a
2 5 NaN
3 6 d

Add total row to dataframe with multi level index

Consider the follow dataframe with a multi level index:
arrays = [np.array(['bar', 'bar', 'baz']),
np.array(['one', 'two', 'one'])]
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
columns=['a', 'b', 'c'],
index=arrays)
All I'm trying to do is add a 'Totals' row to the bottom (12, 15, 18 would be the expected values here). It seems like I need to calculate the totals and then append them to the dataframe, but I just can't get it work while preserving the multi level index (which I want to do). Thanks in advance!

This does not preserve your multi-level index, but it does append a new row called "total" that contains column sums:
import pandas as pd
import numpy as np
arrays = [np.array(['bar', 'bar', 'baz']),
np.array(['one', 'two', 'one'])]
df = pd.DataFrame(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]]),
columns=['a', 'b', 'c'],
index=arrays)
df.append(df.sum().rename('total')).assign(total=lambda d: d.sum(1))

I figured it out. Thanks for the responses. Those plus a little more education about indices in Python got me to something that worked.
# Create df of totals
df2 = pd.DataFrame(df.sum())
# Transpose df
df2 = df2.T
# Reset index
df2 = df2.reset_index()
# Add additional column so the columns of df2 match the columns of df
df2['Index'] = "zTotal"
# Set indices to match df indices
df2 = df2.set_index(['index', 'Index'])
# Concat df and df2
df3 = pd.concat([df, df2])
# Sort in desired order
df3 = df3.sort_index(ascending=[False,True])

plotting a given column name across different data frames in python

All, I have multiple dataframes like
df1 = pd.DataFrame(np.array([
['a', 1, 2],
['b', 3, 4],
['c', 5, 6]]),
columns=['name', 'attr1', 'attr2'])
df2 = pd.DataFrame(np.array([
['a', 2, 3],
['b', 4, 5],
['c', 6, 7]]),
columns=['name', 'attr1', 'attr2'])
df3 = pd.DataFrame(np.array([
['a', 3, 4],
['b', 5, 6],
['c', 7, 8]]),
columns=['name', 'attr1', 'attr2'])
each of these dataframes are generated at specific time steps says T=[t1, t2, t3]
I would like to plot, attr1 or attr2 of the diff data frames as function of time T. I would like to do this for 'a', 'b' and 'c' on all the same graph.
Plot Attr1 VS time for 'a', 'b' and 'c'

If I understand correctly, first assign a column T to each of your dataframes, then concatenate the three. Then, you can groupby the name column, iterate through each, and plot T against attr1 or attr2:
dfs = pd.concat([df1.assign(T=1), df2.assign(T=2), df3.assign(T=3)])
for name, data in dfs.groupby('name'):
plt.plot(data['T'], data['attr2'], label=name)
plt.xlabel('Time')
plt.ylabel('attr2')
plt.legend()
plt.show()