%global _empty_manifest_terminate_build 0
Name: python-fundamentalanalysis
Version: 0.2.14
Release: 1
Summary: Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies.
License: MIT
URL: https://github.com/JerBouma/FundamentalAnalysis
Source0: https://mirrors.nju.edu.cn/pypi/web/packages/19/99/727250aaa91a2cd291363dba950dfcb1967630a8cd29e209592ae1cf8656/fundamentalanalysis-0.2.14.tar.gz
BuildArch: noarch
%description
# Fundamental Analysis
This package collects fundamentals and detailed company stock data from a large group of companies (20.000+)
from FinancialModelingPrep and uses Yahoo Finance to obtain stock data for any financial instrument. It allows
the user to do most of the essential fundamental analysis. It also gives the possibility to quickly compare
multiple companies or do a sector analysis.
To find symbols of specific sectors and/or industries have a look at my [Finance Database](https://github.com/JerBouma/FinanceDatabase) or
see a visualisation of the data on my [Fundamentals Quantifier website](https://github.com/JerBouma/FundamentalsQuantifier).
[](https://www.buymeacoffee.com/jerbouma)
[](https://github.com/JerBouma/FundamentalAnalysis/issues)
[](https://github.com/JerBouma/FundamentalAnalysis/pulls)
[](https://pypi.org/project/FundamentalAnalysis/)
[](https://pypi.org/project/FundamentalAnalysis/)
## Functions
Here you can find a list of the available functions within this package separated per module.
- **details**
- `available_companies` - shows the complete list of companies that are available for fundamental data
gathering including current price, and the exchange the company is listed on. This is an extensive list with
well over 20.000 companies.
- `profile` - gives information about, among other things, the industry, sector exchange
and company description.
- `quote` - provides actual information about the company which is, among other things, the day high,
market cap, open and close price and price-to-equity ratio.
- `enterprise` - displays stock price, number of shares, market capitalization and
enterprise value over time.
- `rating` - based on specific ratios, provides information whether the company is a (strong) buy,
neutral or a (strong) sell.
- `discounted_cash_flow` - calculates the discounted cash flow of a company over time including the
DCF of today.
- `earnings_calendar` - displays information about earnings date of a large selection of symbols this year
including the expected PE ratio.
- **financial_statement**
- `income_statement` - collects a complete income statement over time. This can be either quarterly
or annually.
- `balance_sheet_statement` - collects a complete balance sheet statement over time. This can be either quarterly
or annually.
- `cash_flow_statement` - collects a complete cash flow statement over time. This can be either quarterly
or annually.
- **ratios**
- `key_metrics` - lists the key metrics (in total 57 metrics) of a company over time (annual
and quarterly). This includes, among other things, Return on Equity (ROE), Working Capital,
Current Ratio and Debt to Assets.
- `financial_ratios` - includes in-depth ratios (in total 57 ratios) of a company over time (annual
and quarterly). This contains, among other things, Price-to-Book Ratio, Payout Ratio and Operating Cycle.
- `financial_statement_growth` - measures the growth of several financial statement items and ratios over
time (annual and quarterly). These are, among other things, Revenue Growth (3, 5 and 10 years),
inventory growth and operating cash flow growth (3, 5 and 10 years).
- **stock_data**
- `stock_data` - collects all stock data (including Close, Adjusted Close, High, Low, Open and Volume) of
the provided ticker. This can be any financial instrument.
- `stock_data_detailed` - collects an expansive amount of stock data (including Close, Adjusted Close,
High, Low, Open, Volume, Unadjusted Volume, Absolute Change, Percentage Change, Volume Weighted
Average Price (VWAP), Date Label and Change over Time). The data collection is limited to
the companies listed in the function `available_companies`. Use the `stock_data` function for information about
anything else. (ETFs, Mutual Funds, Options, Indices etc.)
- `stock_dividend` - gives complete information about the company's dividend which includes adjusted dividend, dividend, record date, payment date and declaration date over time. This function only allows company tickers and is limited to the companies found by calling `available_companies` from the details module.
## Installation
1. `pip install fundamentalanalysis`
* Alternatively, download this repository.
2. (within Python) `import fundamentalanalysis as fa`
To be able to use this package you need an API Key from FinancialModellingPrep. Follow the following instructions to
obtain a _free_ API Key. Note that these keys are limited to 250 requests per account. There is no time limit.
1. Go to [FinancialModellingPrep's API](https://financialmodelingprep.com/developer/docs/)
2. Under "Get your Free API Key Today!" click on "Get my API KEY here"
3. Sign-up to the website and select the Free Plan
4. Obtain the API Key as found [here](https://financialmodelingprep.com/developer/docs/)
5. Start using this package.
When you run out of daily requests (250), you have to upgrade to a Premium version. Note that I am in no way
affiliated with FinancialModellingPrep and never will be.
## Example
To collect all annual data about a company, in this case MSFT, you can run the following code:
```python
import fundamentalanalysis as fa
ticker = "MSFT"
api_key = "YOUR API KEY HERE"
# Show the available companies
companies = fa.available_companies(api_key)
# Collect general company information
profile = fa.profile(ticker, api_key)
# Collect recent company quotes
quotes = fa.quote(ticker, api_key)
# Collect market cap and enterprise value
entreprise_value = fa.enterprise(ticker, api_key)
# Show recommendations of Analysts
ratings = fa.rating(ticker, api_key)
# Obtain DCFs over time
dcf_annually = fa.discounted_cash_flow(ticker, api_key, period="annual")
# Collect the Balance Sheet statements
balance_sheet_annually = fa.balance_sheet_statement(ticker, api_key, period="annual")
# Collect the Income Statements
income_statement_annually = fa.income_statement(ticker, api_key, period="annual")
# Collect the Cash Flow Statements
cash_flow_statement_annually = fa.cash_flow_statement(ticker, api_key, period="annual")
# Show Key Metrics
key_metrics_annually = fa.key_metrics(ticker, api_key, period="annual")
# Show a large set of in-depth ratios
financial_ratios_annually = fa.financial_ratios(ticker, api_key, period="annual")
# Show the growth of the company
growth_annually = fa.financial_statement_growth(ticker, api_key, period="annual")
# Download general stock data
stock_data = fa.stock_data(ticker, period="ytd", interval="1d")
# Download detailed stock data
stock_data_detailed = fa.stock_data_detailed(ticker, api_key, begin="2000-01-01", end="2020-01-01")
# Download dividend history
dividends = fa.stock_dividend(ticker, api_key, begin="2000-01-01", end="2020-01-01")
```
Note that quarterly data is not available with a free API key. You should therefore not be able to run this code below without a subscription.
```python
import fundamentalanalysis as fa
ticker = "MSFT"
api_key = "YOUR API KEY HERE"
# Obtain DCFs over time
dcf_quarterly = fa.discounted_cash_flow(ticker, api_key, period="quarter")
# Collect the Balance Sheet statements
balance_sheet_quarterly = fa.balance_sheet_statement(ticker, api_key, period="quarter")
# Collect the Income Statements
income_statement_quarterly = fa.income_statement(ticker, api_key, period="quarter")
# Collect the Cash Flow Statements
cash_flow_statement_quarterly = fa.cash_flow_statement(ticker, api_key, period="quarter")
# Show Key Metrics
key_metrics_quarterly = fa.key_metrics(ticker, api_key, period="quarter")
# Show a large set of in-depth ratios
financial_ratios_quarterly = fa.financial_ratios(ticker, api_key, period="quarter")
# Show the growth of the company
growth_quarterly = fa.financial_statement_growth(ticker, api_key, period="quarter")
```
With this data you can do a complete analysis of the selected company, in this case Microsoft. However, by looping
over a large selection of companies you are able to collect a bulk of data. Therefore, by entering a specific sector
(for example, all tickers of the Semi-Conducter industry) you can quickly quantify the sector and look for
key performers.
To find companies belonging to a specific sector or industry, please have a look at the JSON files
[here](https://github.com/JerBouma/FundamentalsQuantifier/tree/master/data) or use the [Finance Database](https://github.com/JerBouma/FinanceDatabase). Alternatively, you can have a look at the [Fundamentals Quantifier](https://fundamentals-quantifier.herokuapp.com/), a website that I have written to visually compare any selection of companies.
## Contribution
I highly appreciate Pull Requests and Issues Reports as they can greatly improve the package.
%package -n python3-fundamentalanalysis
Summary: Fully-fledged Fundamental Analysis package capable of collecting 20 years of Company Profiles, Financial Statements, Ratios and Stock Data of 20.000+ companies.
Provides: python-fundamentalanalysis
BuildRequires: python3-devel
BuildRequires: python3-setuptools
BuildRequires: python3-pip
%description -n python3-fundamentalanalysis
# Fundamental Analysis
This package collects fundamentals and detailed company stock data from a large group of companies (20.000+)
from FinancialModelingPrep and uses Yahoo Finance to obtain stock data for any financial instrument. It allows
the user to do most of the essential fundamental analysis. It also gives the possibility to quickly compare
multiple companies or do a sector analysis.
To find symbols of specific sectors and/or industries have a look at my [Finance Database](https://github.com/JerBouma/FinanceDatabase) or
see a visualisation of the data on my [Fundamentals Quantifier website](https://github.com/JerBouma/FundamentalsQuantifier).
[](https://www.buymeacoffee.com/jerbouma)
[](https://github.com/JerBouma/FundamentalAnalysis/issues)
[](https://github.com/JerBouma/FundamentalAnalysis/pulls)
[](https://pypi.org/project/FundamentalAnalysis/)
[](https://pypi.org/project/FundamentalAnalysis/)
## Functions
Here you can find a list of the available functions within this package separated per module.
- **details**
- `available_companies` - shows the complete list of companies that are available for fundamental data
gathering including current price, and the exchange the company is listed on. This is an extensive list with
well over 20.000 companies.
- `profile` - gives information about, among other things, the industry, sector exchange
and company description.
- `quote` - provides actual information about the company which is, among other things, the day high,
market cap, open and close price and price-to-equity ratio.
- `enterprise` - displays stock price, number of shares, market capitalization and
enterprise value over time.
- `rating` - based on specific ratios, provides information whether the company is a (strong) buy,
neutral or a (strong) sell.
- `discounted_cash_flow` - calculates the discounted cash flow of a company over time including the
DCF of today.
- `earnings_calendar` - displays information about earnings date of a large selection of symbols this year
including the expected PE ratio.
- **financial_statement**
- `income_statement` - collects a complete income statement over time. This can be either quarterly
or annually.
- `balance_sheet_statement` - collects a complete balance sheet statement over time. This can be either quarterly
or annually.
- `cash_flow_statement` - collects a complete cash flow statement over time. This can be either quarterly
or annually.
- **ratios**
- `key_metrics` - lists the key metrics (in total 57 metrics) of a company over time (annual
and quarterly). This includes, among other things, Return on Equity (ROE), Working Capital,
Current Ratio and Debt to Assets.
- `financial_ratios` - includes in-depth ratios (in total 57 ratios) of a company over time (annual
and quarterly). This contains, among other things, Price-to-Book Ratio, Payout Ratio and Operating Cycle.
- `financial_statement_growth` - measures the growth of several financial statement items and ratios over
time (annual and quarterly). These are, among other things, Revenue Growth (3, 5 and 10 years),
inventory growth and operating cash flow growth (3, 5 and 10 years).
- **stock_data**
- `stock_data` - collects all stock data (including Close, Adjusted Close, High, Low, Open and Volume) of
the provided ticker. This can be any financial instrument.
- `stock_data_detailed` - collects an expansive amount of stock data (including Close, Adjusted Close,
High, Low, Open, Volume, Unadjusted Volume, Absolute Change, Percentage Change, Volume Weighted
Average Price (VWAP), Date Label and Change over Time). The data collection is limited to
the companies listed in the function `available_companies`. Use the `stock_data` function for information about
anything else. (ETFs, Mutual Funds, Options, Indices etc.)
- `stock_dividend` - gives complete information about the company's dividend which includes adjusted dividend, dividend, record date, payment date and declaration date over time. This function only allows company tickers and is limited to the companies found by calling `available_companies` from the details module.
## Installation
1. `pip install fundamentalanalysis`
* Alternatively, download this repository.
2. (within Python) `import fundamentalanalysis as fa`
To be able to use this package you need an API Key from FinancialModellingPrep. Follow the following instructions to
obtain a _free_ API Key. Note that these keys are limited to 250 requests per account. There is no time limit.
1. Go to [FinancialModellingPrep's API](https://financialmodelingprep.com/developer/docs/)
2. Under "Get your Free API Key Today!" click on "Get my API KEY here"
3. Sign-up to the website and select the Free Plan
4. Obtain the API Key as found [here](https://financialmodelingprep.com/developer/docs/)
5. Start using this package.
When you run out of daily requests (250), you have to upgrade to a Premium version. Note that I am in no way
affiliated with FinancialModellingPrep and never will be.
## Example
To collect all annual data about a company, in this case MSFT, you can run the following code:
```python
import fundamentalanalysis as fa
ticker = "MSFT"
api_key = "YOUR API KEY HERE"
# Show the available companies
companies = fa.available_companies(api_key)
# Collect general company information
profile = fa.profile(ticker, api_key)
# Collect recent company quotes
quotes = fa.quote(ticker, api_key)
# Collect market cap and enterprise value
entreprise_value = fa.enterprise(ticker, api_key)
# Show recommendations of Analysts
ratings = fa.rating(ticker, api_key)
# Obtain DCFs over time
dcf_annually = fa.discounted_cash_flow(ticker, api_key, period="annual")
# Collect the Balance Sheet statements
balance_sheet_annually = fa.balance_sheet_statement(ticker, api_key, period="annual")
# Collect the Income Statements
income_statement_annually = fa.income_statement(ticker, api_key, period="annual")
# Collect the Cash Flow Statements
cash_flow_statement_annually = fa.cash_flow_statement(ticker, api_key, period="annual")
# Show Key Metrics
key_metrics_annually = fa.key_metrics(ticker, api_key, period="annual")
# Show a large set of in-depth ratios
financial_ratios_annually = fa.financial_ratios(ticker, api_key, period="annual")
# Show the growth of the company
growth_annually = fa.financial_statement_growth(ticker, api_key, period="annual")
# Download general stock data
stock_data = fa.stock_data(ticker, period="ytd", interval="1d")
# Download detailed stock data
stock_data_detailed = fa.stock_data_detailed(ticker, api_key, begin="2000-01-01", end="2020-01-01")
# Download dividend history
dividends = fa.stock_dividend(ticker, api_key, begin="2000-01-01", end="2020-01-01")
```
Note that quarterly data is not available with a free API key. You should therefore not be able to run this code below without a subscription.
```python
import fundamentalanalysis as fa
ticker = "MSFT"
api_key = "YOUR API KEY HERE"
# Obtain DCFs over time
dcf_quarterly = fa.discounted_cash_flow(ticker, api_key, period="quarter")
# Collect the Balance Sheet statements
balance_sheet_quarterly = fa.balance_sheet_statement(ticker, api_key, period="quarter")
# Collect the Income Statements
income_statement_quarterly = fa.income_statement(ticker, api_key, period="quarter")
# Collect the Cash Flow Statements
cash_flow_statement_quarterly = fa.cash_flow_statement(ticker, api_key, period="quarter")
# Show Key Metrics
key_metrics_quarterly = fa.key_metrics(ticker, api_key, period="quarter")
# Show a large set of in-depth ratios
financial_ratios_quarterly = fa.financial_ratios(ticker, api_key, period="quarter")
# Show the growth of the company
growth_quarterly = fa.financial_statement_growth(ticker, api_key, period="quarter")
```
With this data you can do a complete analysis of the selected company, in this case Microsoft. However, by looping
over a large selection of companies you are able to collect a bulk of data. Therefore, by entering a specific sector
(for example, all tickers of the Semi-Conducter industry) you can quickly quantify the sector and look for
key performers.
To find companies belonging to a specific sector or industry, please have a look at the JSON files
[here](https://github.com/JerBouma/FundamentalsQuantifier/tree/master/data) or use the [Finance Database](https://github.com/JerBouma/FinanceDatabase). Alternatively, you can have a look at the [Fundamentals Quantifier](https://fundamentals-quantifier.herokuapp.com/), a website that I have written to visually compare any selection of companies.
## Contribution
I highly appreciate Pull Requests and Issues Reports as they can greatly improve the package.
%package help
Summary: Development documents and examples for fundamentalanalysis
Provides: python3-fundamentalanalysis-doc
%description help
# Fundamental Analysis
This package collects fundamentals and detailed company stock data from a large group of companies (20.000+)
from FinancialModelingPrep and uses Yahoo Finance to obtain stock data for any financial instrument. It allows
the user to do most of the essential fundamental analysis. It also gives the possibility to quickly compare
multiple companies or do a sector analysis.
To find symbols of specific sectors and/or industries have a look at my [Finance Database](https://github.com/JerBouma/FinanceDatabase) or
see a visualisation of the data on my [Fundamentals Quantifier website](https://github.com/JerBouma/FundamentalsQuantifier).
[](https://www.buymeacoffee.com/jerbouma)
[](https://github.com/JerBouma/FundamentalAnalysis/issues)
[](https://github.com/JerBouma/FundamentalAnalysis/pulls)
[](https://pypi.org/project/FundamentalAnalysis/)
[](https://pypi.org/project/FundamentalAnalysis/)
## Functions
Here you can find a list of the available functions within this package separated per module.
- **details**
- `available_companies` - shows the complete list of companies that are available for fundamental data
gathering including current price, and the exchange the company is listed on. This is an extensive list with
well over 20.000 companies.
- `profile` - gives information about, among other things, the industry, sector exchange
and company description.
- `quote` - provides actual information about the company which is, among other things, the day high,
market cap, open and close price and price-to-equity ratio.
- `enterprise` - displays stock price, number of shares, market capitalization and
enterprise value over time.
- `rating` - based on specific ratios, provides information whether the company is a (strong) buy,
neutral or a (strong) sell.
- `discounted_cash_flow` - calculates the discounted cash flow of a company over time including the
DCF of today.
- `earnings_calendar` - displays information about earnings date of a large selection of symbols this year
including the expected PE ratio.
- **financial_statement**
- `income_statement` - collects a complete income statement over time. This can be either quarterly
or annually.
- `balance_sheet_statement` - collects a complete balance sheet statement over time. This can be either quarterly
or annually.
- `cash_flow_statement` - collects a complete cash flow statement over time. This can be either quarterly
or annually.
- **ratios**
- `key_metrics` - lists the key metrics (in total 57 metrics) of a company over time (annual
and quarterly). This includes, among other things, Return on Equity (ROE), Working Capital,
Current Ratio and Debt to Assets.
- `financial_ratios` - includes in-depth ratios (in total 57 ratios) of a company over time (annual
and quarterly). This contains, among other things, Price-to-Book Ratio, Payout Ratio and Operating Cycle.
- `financial_statement_growth` - measures the growth of several financial statement items and ratios over
time (annual and quarterly). These are, among other things, Revenue Growth (3, 5 and 10 years),
inventory growth and operating cash flow growth (3, 5 and 10 years).
- **stock_data**
- `stock_data` - collects all stock data (including Close, Adjusted Close, High, Low, Open and Volume) of
the provided ticker. This can be any financial instrument.
- `stock_data_detailed` - collects an expansive amount of stock data (including Close, Adjusted Close,
High, Low, Open, Volume, Unadjusted Volume, Absolute Change, Percentage Change, Volume Weighted
Average Price (VWAP), Date Label and Change over Time). The data collection is limited to
the companies listed in the function `available_companies`. Use the `stock_data` function for information about
anything else. (ETFs, Mutual Funds, Options, Indices etc.)
- `stock_dividend` - gives complete information about the company's dividend which includes adjusted dividend, dividend, record date, payment date and declaration date over time. This function only allows company tickers and is limited to the companies found by calling `available_companies` from the details module.
## Installation
1. `pip install fundamentalanalysis`
* Alternatively, download this repository.
2. (within Python) `import fundamentalanalysis as fa`
To be able to use this package you need an API Key from FinancialModellingPrep. Follow the following instructions to
obtain a _free_ API Key. Note that these keys are limited to 250 requests per account. There is no time limit.
1. Go to [FinancialModellingPrep's API](https://financialmodelingprep.com/developer/docs/)
2. Under "Get your Free API Key Today!" click on "Get my API KEY here"
3. Sign-up to the website and select the Free Plan
4. Obtain the API Key as found [here](https://financialmodelingprep.com/developer/docs/)
5. Start using this package.
When you run out of daily requests (250), you have to upgrade to a Premium version. Note that I am in no way
affiliated with FinancialModellingPrep and never will be.
## Example
To collect all annual data about a company, in this case MSFT, you can run the following code:
```python
import fundamentalanalysis as fa
ticker = "MSFT"
api_key = "YOUR API KEY HERE"
# Show the available companies
companies = fa.available_companies(api_key)
# Collect general company information
profile = fa.profile(ticker, api_key)
# Collect recent company quotes
quotes = fa.quote(ticker, api_key)
# Collect market cap and enterprise value
entreprise_value = fa.enterprise(ticker, api_key)
# Show recommendations of Analysts
ratings = fa.rating(ticker, api_key)
# Obtain DCFs over time
dcf_annually = fa.discounted_cash_flow(ticker, api_key, period="annual")
# Collect the Balance Sheet statements
balance_sheet_annually = fa.balance_sheet_statement(ticker, api_key, period="annual")
# Collect the Income Statements
income_statement_annually = fa.income_statement(ticker, api_key, period="annual")
# Collect the Cash Flow Statements
cash_flow_statement_annually = fa.cash_flow_statement(ticker, api_key, period="annual")
# Show Key Metrics
key_metrics_annually = fa.key_metrics(ticker, api_key, period="annual")
# Show a large set of in-depth ratios
financial_ratios_annually = fa.financial_ratios(ticker, api_key, period="annual")
# Show the growth of the company
growth_annually = fa.financial_statement_growth(ticker, api_key, period="annual")
# Download general stock data
stock_data = fa.stock_data(ticker, period="ytd", interval="1d")
# Download detailed stock data
stock_data_detailed = fa.stock_data_detailed(ticker, api_key, begin="2000-01-01", end="2020-01-01")
# Download dividend history
dividends = fa.stock_dividend(ticker, api_key, begin="2000-01-01", end="2020-01-01")
```
Note that quarterly data is not available with a free API key. You should therefore not be able to run this code below without a subscription.
```python
import fundamentalanalysis as fa
ticker = "MSFT"
api_key = "YOUR API KEY HERE"
# Obtain DCFs over time
dcf_quarterly = fa.discounted_cash_flow(ticker, api_key, period="quarter")
# Collect the Balance Sheet statements
balance_sheet_quarterly = fa.balance_sheet_statement(ticker, api_key, period="quarter")
# Collect the Income Statements
income_statement_quarterly = fa.income_statement(ticker, api_key, period="quarter")
# Collect the Cash Flow Statements
cash_flow_statement_quarterly = fa.cash_flow_statement(ticker, api_key, period="quarter")
# Show Key Metrics
key_metrics_quarterly = fa.key_metrics(ticker, api_key, period="quarter")
# Show a large set of in-depth ratios
financial_ratios_quarterly = fa.financial_ratios(ticker, api_key, period="quarter")
# Show the growth of the company
growth_quarterly = fa.financial_statement_growth(ticker, api_key, period="quarter")
```
With this data you can do a complete analysis of the selected company, in this case Microsoft. However, by looping
over a large selection of companies you are able to collect a bulk of data. Therefore, by entering a specific sector
(for example, all tickers of the Semi-Conducter industry) you can quickly quantify the sector and look for
key performers.
To find companies belonging to a specific sector or industry, please have a look at the JSON files
[here](https://github.com/JerBouma/FundamentalsQuantifier/tree/master/data) or use the [Finance Database](https://github.com/JerBouma/FinanceDatabase). Alternatively, you can have a look at the [Fundamentals Quantifier](https://fundamentals-quantifier.herokuapp.com/), a website that I have written to visually compare any selection of companies.
## Contribution
I highly appreciate Pull Requests and Issues Reports as they can greatly improve the package.
%prep
%autosetup -n fundamentalanalysis-0.2.14
%build
%py3_build
%install
%py3_install
install -d -m755 %{buildroot}/%{_pkgdocdir}
if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
pushd %{buildroot}
if [ -d usr/lib ]; then
find usr/lib -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/lib64 ]; then
find usr/lib64 -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/bin ]; then
find usr/bin -type f -printf "/%h/%f\n" >> filelist.lst
fi
if [ -d usr/sbin ]; then
find usr/sbin -type f -printf "/%h/%f\n" >> filelist.lst
fi
touch doclist.lst
if [ -d usr/share/man ]; then
find usr/share/man -type f -printf "/%h/%f.gz\n" >> doclist.lst
fi
popd
mv %{buildroot}/filelist.lst .
mv %{buildroot}/doclist.lst .
%files -n python3-fundamentalanalysis -f filelist.lst
%dir %{python3_sitelib}/*
%files help -f doclist.lst
%{_docdir}/*
%changelog
* Fri May 05 2023 Python_Bot - 0.2.14-1
- Package Spec generated