What is the difference between parenthesis and no parenthesis in head and tail?

[kulimer]

My first week using Python. I am a little confused about if I should use parenthesis or not when doing head and tail

Suppose I have variable df as a data frame in NumPy

df.head vs df.head() vs df.head(5) 

I understand the 5 means show first 5 rows, but how do I know if I should use df.head or df.head(), though df.head() gives me what I want.

But, why the following. This would be correct

df.column

This

df.column()

would give me an error message.

Why such inconsistency? I am so confused!

[nilamo]

Parenthesis are always required for functions/methods/instantiating objects.

You're obviously using numpy, which does a few magic things with attribute lookup to help make working with large datasets easier (which means, for numpy, not using parenthesis is the right choice).

[kulimer]

Wait, you say "not using parenthesis is the right choice"?
1. df.head, gives me a bunch of data, NOT just the first few rows. It's not what I want!

df.head() gives the first few rows. This is right.

2. df.column is right. df.column() wrong.

1 and 2 are both in NumPy, no?

[Larz60+]

Maybe this will help you understand a bit more:

def ziggy():
   print('hello ziggy')

def zaggy():
   print('Hello zaggy, do you know ziggy?')

xx = ziggy
xx()

xx= zaggy
xx()

results:

Output:
hello ziggy
Hello zaggy, do you know ziggy?

using a function name without () returns the address of the function.
therefore xx = ziggy assigns xx to the address of ziggy

if you add parenthesis, the function will be executed, therefore
in above example xx() will execute the function whose address is stored in it at the time it is called with ()

[kulimer]

Thank you! It looks like Python still has that address and pointer stuff. I thought it is object-oriented. I think in Java you don't get this kind of address problem.

In this day, who would want to know the address of the memory. We just want to get the work done.

[snippsat]

Thank you! It looks like Python still has that address and pointer stuff

This has nothing to do about pointer stuff,that Python don't have.
You can assign to df.columns = that's why it's not a function call.
df.head() is just a function call to get rows,set to default 5.
Eg:

import pandas as pd

df = pd.DataFrame({'a':[1,2,3,4], 'b': [10,20,30,40], 'c': [5,6,7,8], 'd': [50,60,70,80]})
# Set columns,would look bad with ()
df.columns = ['a', 'b', 'c', 'd']
print(df)

a b c d
0 1 10 5 50
1 2 20 6 60
2 3 30 7 70
3 4 40 8 80

# caLL 2 rows
print(df.head(2))

a b c d
0 1 10 5 50
1 2 20 6 60

I think in Java you don't get this kind of address problem.

It's not a problem as explained,
Java has it's own bundle of verbose stupidness

[Larz60+]

I assure you, if Java runs on a computer, it uses addresses and pointers.
You just don't see them as such, Python as well.
Even if the code is stored as pcode, once it's compiled it will use them.

A pointer is an indirect address, or an offset, which can be a register or a memory location
the 'address' (since it is indirect) points to another memory address, or an offset from that address
This allows changing what the 'pointer' points to by using replacement or some math

Without them, tables or lists and other constructs would be very difficult to construct, and slow.

[volcano63]

Thank you! It looks like Python still has that address and pointer stuff

This has nothing to do about pointer stuff,that Python don't have.
You can assign to df.columns = that's why it's not a function call.

Here's our hint - the guy comes from   Java.

Object attributes - which in C++ are called member variables (thankfully   , I don't know Java equivalent) may be assigned and referenced directly, without setter/getter monstrosities Java heretics are used to   .

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In this day, who would want to know the address of the memory. We just want to get the work done.

Python probably gives the best TTM - Time-To-Market - results. Much better than Java and C++. You just have to understand that it's neither....

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Suppose I have variable df as a data frame in NumPy

df is an object