Make sure to keep the below packages updated and execute the pip's on your local system. 

pip install pandas==1.1.2
pip install openpyxl==3.0.5
pip install xlrd==1.2.0
pip install hypothesis
pip install jupyterlab==3.0.0b4
pip install bottleneck
pip install numexpr
pip install python-dateutil
pip install pytz
pip install NumPy
pip install setuptools
pip install pytest
pip install BeautifulSoup4==4.9.1 --use-feature=2020-resolver

side notes: https://blog.python.org/2020/07/upgrade-pip-20-2-changes-20-3.html

Execute these commands: python -m pip install --upgrade pip, pip check and finally install your package using pip install --use-feature=2020-resolver. This will install the correct versions for the dependencies. – arun Aug 31 at 18:41</p>

installation dependency https://pandas.pydata.org/docs/getting_started/install.html

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Code taken from https://pandas.pydata.org/docs/getting_started/intro_tutorials/01_table_oriented.html

import pandas as pd

df = pd.DataFrame({
            "Name": ["Braund, Mr. Owen Harris",
            "Allen, Mr. William Henry",
            "Bonnell, Miss. Elizabeth"],
            "Age": [22, 35, 58],
            "Sex": ["male", "male", "female"]}
   )

df
Name Age Sex
0 Braund, Mr. Owen Harris 22 male
1 Allen, Mr. William Henry 35 male
2 Bonnell, Miss. Elizabeth 58 female 
df["Age"]

0    22
1    35
2    58
Name: Age, dtype: int64
ages = pd.Series([22, 35, 58], name="Age")

ages

0    22
1    35
2    58
Name: Age, dtype: int64
df["Age"].max()

58
ages.max()

58
 df.describe()
Age
count 3.000000
mean 38.333333
std 18.230012
min 22.000000
25% 28.500000
50% 35.000000
75% 46.500000
max 58.000000
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