<jupyter_start><jupyter_text>[Python Pandas For Data Discovery in 7 Simple Steps](https://www.kdnuggets.com/2020/03/python-pandas-data-discovery.html) [How to select rows and columns in Pandas using [ ], .loc, iloc, .at and .iat](https://www.kdnuggets.com/2019/06/select-rows-columns-pandas.html)[The Essential Toolbox for Data Cleaning](https://www.kdnuggets.com/2019/12/essential-toolbox-data-cleaning.html)[Why and How to Use Pandas with Large Data](https://towardsdatascience.com/why-and-how-to-use-pandas-with-large-data-9594dda2ea4c) [10 Free Top Notch Machine Learning Courses](https://www.kdnuggets.com/2019/12/10-free-top-notch-courses-machine-learning.html) [A Practical Approach to Machine Learning - Python, NumPy, Pandas, ML Basics Logistic regressison, etc](https://practicalai.me/)<jupyter_code>import pandas as pd 
import numpy as np 
  
# making list of values 
values = [498.49, np.nan, 994.97, 564.59, 783.01] 

  
# making series from list 
series = pd.Series(values) 
print(series) 

# calling method 
#cumsum = series.cumsum() 
#dtype(cumsum)
  
# concatenating     
#df = pd.DataFrame(series, cumsum)
#print(df)<jupyter_output>0    498.49
1       NaN
2    994.97
3    564.59
4    783.01
dtype: float64
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