![Difference Between [] and [[]] in Python](https://docuprint.cloud68.co/uploads/difference-between-and-in-python_1200.jpg)
I am taking a course on Coursera titled "Data Analysis with Python". I am new to Python. I have some experience with C and MATLAB. That's why I haven't faced that much problem except for one thing.
At first please take a look at the following code.
import pandas as pd
#path of data
path = '
df = pd.read_csv(path)
from sklearn.linear_model import LinearRegression
lm = LinearRegression()
X = df[['highway-mpg']] #'highway-mpg' is a column in the dataframe
Y = df['price'] #'price' is a column in the dataframe
lm.fit(X,Y)
Here while defining X they have used double square brackets [[]] and in the case of Y used []. I know that square brackets are used for listing in Python and nested listing is possible. But it doesn't seem to be a nested list. Can you please differentiate between them?
5 Answers
One does slice the dataframe and return a sub-dataframe, the other select exactly one column and gives it as a Series:
If you pass a list of string to the brackets
[['a', 'c']], you'll obtainDataFramewhich contains only the column you askedIf you pass a string to the brackets
['a'], you'll obtain only aSerieswhich correponds to the name you give
Example
df = pd.DataFrame([
{'a': 1, 'b': 2, 'c': 4},
{'a': 3, 'b': 4, 'c': 6},
])
print(df['a'])
# That is a Series
0 1
1 3
Name: a, dtype: int64
# -------------------------------------------
print(df[['a']])
# That is a DataFrame
a
0 1
1 3
# -------------------------------------------
print(df[['a', 'c']])
# That is a DataFrame
a c
0 1 4
1 3 6
when you want to use or return a single column of the data frame i.e. returns a Series then use df['xyz'] in case you want to return or use multiple columns you can use df[['xyz','abc','ijk']] which returns another data frame with specified columns.
Note
df[['xyz']]this is allowed, butdf['xyz','abc','ijk']this is not allowed.
df1=pd.DataFrame({"Col":[1,2,3],"Row":[2,3,4],"value":[6,5,7],"ree":[0,0,0]})
df1
Col Row value ree
0 1 2 6 0
1 2 3 5 0
2 3 4 7 0
df1['Col']
0 1
1 2 # Series object
2 3
Name: Col, dtype: int64
df1[['Col']]
Col
0 1
1 2 # DataFrame object
2 3
df1[["Col","Row","value"]]
Col Row value
0 1 2 6
1 2 3 5 # DataFrame object
2 3 4 7
in your case X['h___...'] can also be used as there is only one column
[[]] is generally used to pass multiple columns to retrieve from a dataframe. Another key distinction is it returns a DataFrame
[] is generally used to pass a single column to retrieve from a dataframe. Another key distinction is it returns a Series
This is one of the key differences when indexing a dataframe. Both perform the same intended operation with different return types
In Machine Learning and Data Analysis, usually X denotes independent variables and y denotes dependent variable. The goal is to predict single dependent variable y using two or many independent variables X. And, In Pandas [[]] is used to get variable number of columns. That is why [[]] used for X and [] for y
[[colname1,colname2,colnamen]] so you are selecting multiple columns from the dataframe whereas ["target_name"] you only need to specify the name of the column since it's just one.
