Python
Python
import pandas as pd
s1= pd.Series([2,3,4,5,6,7,8,9])


print(s1)
0    2
1    3
2    4
3    5
4    6
5    7
6    8
7    9
dtype: int64

Python
import matplotlib.pyplot as plt
x=[1,2,3,4]
y=[5,10,15,20]
plt.bar(x,y,color="red",edgecolor="black")
plt.show()
Python
import pandas as pd
sh= {"name":["akash","suraj","nitesh","qman","suresh","mike","anuv"],
"grade":["A","B","C","D","A","E","B"]
    ,"subject":["acc","bst","eng","maths","social","hindi","acc"],
    "id":[2,3,4,5,6,7,1],"year":[2018,2019,2018,2017,2015,2020,2017]
}
dh=pd.DataFrame(sh)
print(dh)
name grade subject  id  year
0   akash     A     acc   2  2018
1   suraj     B     bst   3  2019
2  nitesh     C     eng   4  2018
3    qman     D   maths   5  2017
4  suresh     A  social   6  2015
5    mike     E   hindi   7  2020
6    anuv     B     acc   1  2017

Python
import pandas as pd
sh= {"name":["akash","suraj","nitesh","qman","suresh","mike","anuv"],
"grade":["A","B","C","D","A","E","B"]
    ,"subject":["acc","bst","eng","maths","social","hindi","acc"],
    "id":[2,3,4,5,6,7,1],"year":[2018,2019,2018,2017,2015,2020,2017]
}
dh=pd.DataFrame(sh)
dh.subject[2]= "aandbhat"
del dh.drop[4]
print(dh)
script.py:8: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!
You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.
A typical example is when you are setting values in a column of a DataFrame, like:

df["col"][row_indexer] = value

Use `df.loc[row_indexer, "col"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy

  dh.subject[2]= "aandbhat"
script.py:8: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame

See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  dh.subject[2]= "aandbhat"
line 9, in <module>
    del dh.drop[4]
        ~~~~~~~^^^
TypeError: 'method' object does not support item deletion
Python
import pandas as pd
x= [2,3,4,5,]
ak= pd.DataFrame(x)
z=[4,5,7,8]
ka=pd.DataFrame(z)
a=ak.append(ka)
y=pd.DataFrame(a)
print(y)
line 6, in <module>
    a=ak.append(ka)
      ^^^^^^^^^
  File "/lib/python3.12/site-packages/pandas/core/generic.py", line 6293, in __getattr__
    return object.__getattribute__(self, name)
           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
AttributeError: 'DataFrame' object has no attribute 'append'. Did you mean: '_append'?
Python