cpi | Quickly adjust US dollars for inflation using the Consumer Price Index (CPI)

by palewire Jupyter Notebook Version: 1.1.6 License: MIT

X-Ray Key Features Code Snippets(10)Community Discussions Vulnerabilities Install Support

kandi X-RAY | cpi Summary

cpi is a Jupyter Notebook library typically used in Bitcoin applications. cpi has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

A Python library that quickly adjusts U.S. dollars for inflation using the Consumer Price Index (CPI).

Support

Quality

Security

License

Reuse

Support

cpi has a low active ecosystem.

It has 114 star(s) with 23 fork(s). There are 5 watchers for this library.

There were 1 major release(s) in the last 6 months.

There are 7 open issues and 40 have been closed. On average issues are closed in 483 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of cpi is 1.1.6

Quality

cpi has no bugs reported.

Security

cpi has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

License

cpi is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

cpi releases are available to install and integrate.

Installation instructions, examples and code snippets are available.

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cpi Key Features

No Key Features are available at this moment for cpi.

cpi Examples and Code Snippets

How to correctly find which rows from a df have a time value that matches with a time interval on Python? Pandas related

Python

Lines of Code : 27

License : Strong Copyleft (CC BY-SA 4.0)

Copy

mask = pd.to_datetime(df['Time']).between('4:30', '8:59')
filtered = df[mask]

>>> filtered
     Time Currency           Volatility expected                                          Event
24  04:30      GBP

Sum up data based on multiple factors in a dataframe

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

df.groupby(['departments', 'shop','week'])

Calculate year-over-year growth rate based on month-over-month's

Python

Lines of Code : 59

License : Strong Copyleft (CC BY-SA 4.0)

Copy

df['MoM%'].add(1).rolling(12).apply(lambda x: x.prod()) - 1

0          NaN
1          NaN
2          NaN
3          NaN
4          NaN
5          NaN
6          NaN
7          NaN
8          NaN
9          NaN
10         NaN
11         Na

Improve running time when using inflation method on pandas

Python

Lines of Code : 7

License : Strong Copyleft (CC BY-SA 4.0)

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all_years = df["year"].unique()
dict_years = {}
for year in all_years:
    dict_years[year] = cpi.inflate(1.0, year)

df['real_fare'] = # apply here: dict_years[row['year']]*row['fare']

Code to stationarize multiple columns in time series

Python

Lines of Code : 2

License : Strong Copyleft (CC BY-SA 4.0)

Copy

df = pd.concat([df, df[["FCI", "CSI", "IPI"]].diff().add_suffix("_diff")], axis=1)

Multiplying pandas DataFrames of different size

Python

Lines of Code : 20

License : Strong Copyleft (CC BY-SA 4.0)

Copy

y = df1['year'].map(df2.set_index('year')['cpi']).fillna(1)
df1.iloc[:, 1:] = df1.iloc[:, 1:].mul(y, axis=0)
print (df1)
   year   firm  assets    ppe  other_variable
0  2000  1.000   50.00   2.00           5.000
1  2001  1.010   60.60   3

Why is linear read-shuffled write not faster than shuffled read-linear write?

Python

Lines of Code : 18

License : Strong Copyleft (CC BY-SA 4.0)

Copy

for i in a:
    c[i] = b[i]

#1. (iteration 1) c[0] = b[0]
1a. read memory at b[0] and store result in register c0
1b. write register c0 at memory address c[0]
#2. (iteration 2) c[1] = b[1]
2a. read memory at b[1] a

How to compare one row from df1 from other rows from df2 based on some condition in pandas?

Python

Lines of Code : 29

License : Strong Copyleft (CC BY-SA 4.0)

Copy

df = pd.DataFrame({
    'Started': [*np.repeat(pd.Timestamp(2018, 9, 12, 12, 12, 21), 2)],
    '%CPI': [0.0, 0.0],
    '%MEM': [0.0, 0.0],
    'COMMAND': ['init', 'kthreadd']
})

    Started %CPI    %MEM    COMMAND

Matrix vector operations in Python

Python

Lines of Code : 13

License : Strong Copyleft (CC BY-SA 4.0)

Copy

number_of_payments = [
    [0, 1, 0, 1, 1, 1, 0, 5, 1, 0, 2, 1],
    [0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 1, 0],
    [1, 3, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0]
]
NDD_month = [8, 7, 11]
dates = []
for i in range(len(number_of_payments)):
    dates.appe

In Pandas, how to make a PivotTable for counting and skip replicates?

Python

Lines of Code : 6

License : Strong Copyleft (CC BY-SA 4.0)

Copy

df.groupby('NomeMembro')['SiglaComissao'].nunique()

NomeMembro
Abelardo Camarinha     6
Vaz de Lima           11

Community Discussions

No Community Discussions are available at this moment for cpi.Refer to stack overflow page for discussions.

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install cpi

The library can be installed from the Python Package Index with any of the standard Python installation tools.

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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