How to do similar to conditional countifs on a dataframe

import pandas as pd
df = pd.read_csv("gas.csv")
display(df['Technology'].value_counts())
print('------')
display(df['Technology'].value_counts()[0]) # This is how you access count of CCGT
display(df['Technology'].value_counts()[1])

Output:

CCGT    47
COAL     2
Name: Technology, dtype: int64

------

47

2

By the way: pd.cut or pd.qcut can be used to calculate quantiles. You don’t have to manually define what a quantile is.

Refer to the documentation and other websites:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.qcut.html

https://www.geeksforgeeks.org/how-to-use-pandas-cut-and-qcut/

There are many methods you can pass to rank. Refer to documentation:

1. https://pandas.pydata.org/docs/reference/api/pandas.Series.rank.html

df['rank'] = df.groupby("Technology")["Average Rank"].rank(method = "dense", ascending = True)
df

method{‘average’, ‘min’, ‘max’, ‘first’, ‘dense’}, default ‘average’

How to rank the group of records that have the same value (i.e. ties):

    average: average rank of the group

    min: lowest rank in the group

    max: highest rank in the group

    first: ranks assigned in order they appear in the array

    dense: like ‘min’, but rank always increases by 1 between groups.