How Are Iloc and Loc Different

>>> s = pd.Series(list("abcdef"), index=[49, 48, 47, 0, 1, 2]) 
49    a
48    b
47    c
0     d
1     e
2     f

>>> s.loc[0]    # value at index label 0
'd'

>>> s.iloc[0]   # value at index location 0
'a'

>>> s.loc[0:1]  # rows at index labels between 0 and 1 (inclusive)
0    d
1    e

>>> s.iloc[0:1] # rows at index location between 0 and 1 (exclusive)
49    a

<object>	description	s.loc[<object>]	s.iloc[<object>]
0	single item	Value at index label 0 (the string 'd')	Value at index location 0 (the string 'a')
0:1	slice	Two rows (labels 0 and 1)	One row (first row at location 0)
1:47	slice with out-of-bounds end	Zero rows (empty Series)	Five rows (location 1 onwards)
1:47:-1	slice with negative step	three rows (labels 1 back to 47)	Zero rows (empty Series)
[2, 0]	integer list	Two rows with given labels	Two rows with given locations
s > 'e'	Bool series (indicating which values have the property)	One row (containing 'f')	NotImplementedError
(s>'e').values	Bool array	One row (containing 'f')	Same as loc
999	int object not in index	KeyError	IndexError (out of bounds)
-1	int object not in index	KeyError	Returns last value in s
lambda x: x.index[3]	callable applied to series (here returning 3rd item in index)	s.loc[s.index[3]]	s.iloc[s.index[3]]

Change values in DataFrame - .iloc vs .loc

First of all. Don't use a for loop with dataframes if you really really have to.
Just use a boolean array to filter your dataframe with loc and assign your values that way.
You can do what you want with a simple merge.

df1 = df1.merge(df2, on='KEY', how='left').rename(columns={'value_alternative': 'value 2'})
df1.loc[df1['value 2'].isna(), 'value 2'] = df1['value']

Reason for iloc not working with assignment is in pandas you can't set a value in a copy of a dataframe. Pandas does this in order to work fast. To have access to the underlying data you need to use loc for filtering. Don't forget loc and iloc do different things. loc looks at the lables of the index while iloc looks at the index number.

In order for this to work you also have to delete the

df1["value 2"] = "nothing"

line from your program

pandas loc vs. iloc vs. at vs. iat?

loc: only work on index

iloc: work on position

at: get scalar values. It's a very fast loc

iat: Get scalar values. It's a very fast iloc

Also,

at and iat are meant to access a scalar, that is, a single element
in the dataframe, while loc and iloc are ments to access several
elements at the same time, potentially to perform vectorized
operations.

http://pyciencia.blogspot.com/2015/05/obtener-y-filtrar-datos-de-un-dataframe.html

Pandas iloc returns different range than loc

As it mentioned in docs for loc:

Warning: Note that contrary to usual python slices, both the start and
the stop are included

On the other hand, iloc do selects based on integer-location based indexing, so it doesn't include stop index.

What would be the syntactical classification of 'loc' and 'iloc' in pandas?

Both LOC and ILOC are methods as they're associated with the Pandas module.

To access values from rows and columns within a Dataframe, both LOC and ILOC are used. One can use these methods to filter and modify values within DF.

LOC - loc() is a label-based data selecting method which means that we have to pass the name of the row or column which we want to select. This method includes the last element of the range passed in it, unlike iloc().

ILOC - iloc() is an indexed-based selecting method which means that we have to pass integer index in the method to select a specific row/column. This method does not include the last element of the range passed in it unlike loc()

Example:

import numpy as np
import pandas as pd
df = pd.DataFrame(np.random.randint(10,100, (5, 4)), columns = list("ABCD"))

df.loc[1:3, "A":"C"]

before the comma, the colon takes row selections and after the comma, the colon takes column selections, here we've to specify the labels of the rows as well as the columns

df.iloc[1:3, 1:3] 

before the comma, the colon takes row selections and after a comma, the colon takes column selections, here we've to specify the index positions of the rows as well as the columns

What is the different between df.loc[anything].index and iloc?

df.loc returns data based on labels (index, columns names). iloc returns data based purely on position (index position, column position) starting from 0.

Your first line of code is creating a slice of the dataframe based on the condition. df.index returned the index of the slice.

df.loc[df['c']==5].index
Int64Index([3, 8], dtype='int64')

The second line, since you passed only one value, pandas assumed it to be index and returns all the elements at the specified index.

df.iloc[3]

a    1
b    1
c    5
d    5

Once you dropped the index number 3, df.iloc[3] will once again return 4th row as the 4th position still exists. On the other hand, using loc will throw keyerror as the dataframe does not have index number 3 in the data anymore.

df.loc[3]
KeyError: 'the label [3] is not in the [index]'

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