### Install fredapi using pip

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

This command installs the fredapi library using pip. Ensure you have pip installed and accessible in your environment.

```sh
pip install fredapi
```

--------------------------------

### GET /series/first_release

Source: https://context7.com/mortada/fredapi/llms.txt

Retrieves the initial release data values for a series, excluding subsequent revisions.

```APIDOC
## GET /series/first_release

### Description
Retrieves only the first-release data values for a series, ignoring all subsequent revisions. This is useful for backtesting and understanding what data was initially reported.

### Method
GET

### Parameters
#### Query Parameters
- **series_id** (string) - Required - The FRED series ID (e.g., 'GDP')

### Response
#### Success Response (200)
- **data** (Series) - A pandas Series containing the first-release values indexed by date.
```

--------------------------------

### GET /series/all_releases

Source: https://context7.com/mortada/fredapi/llms.txt

Retrieves the complete revision history for a specific economic series.

```APIDOC
## GET /series/all_releases

### Description
Retrieves the complete revision history for a series, including all first releases and subsequent revisions.

### Method
GET

### Parameters
#### Query Parameters
- **series_id** (string) - Required - The FRED series ID
- **realtime_start** (string) - Optional - Start date for the revision history
- **realtime_end** (string) - Optional - End date for the revision history

### Response
#### Success Response (200)
- **data** (DataFrame) - A DataFrame with columns: realtime_start, date, and value.
```

--------------------------------

### GET /search

Source: https://context7.com/mortada/fredapi/llms.txt

Performs a fulltext search across all FRED series.

```APIDOC
## GET /search

### Description
Performs a fulltext search across all FRED series. Returns a DataFrame with information about matching series.

### Method
GET

### Parameters
#### Query Parameters
- **search_text** (string) - Required - The search query
- **limit** (int) - Optional - Number of results to return
- **order_by** (string) - Optional - Field to sort by (e.g., 'popularity')
- **sort_order** (string) - Optional - 'asc' or 'desc'

### Response
#### Success Response (200)
- **data** (DataFrame) - A DataFrame containing series metadata like title, frequency, and popularity.
```

--------------------------------

### Get first release of a series

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

Retrieves only the first release of a specified economic data series, ignoring any subsequent revisions. This is useful for historical analysis of initial data points.

```python
data = fred.get_series_first_release('GDP')
```

--------------------------------

### Get Series Data As of Date (Python)

Source: https://context7.com/mortada/fredapi/llms.txt

Returns the data for a series as it was known on a specific historical date, including revisions up to that point but ignoring later ones. Essential for point-in-time analysis. Requires 'fredapi' and an API key.

```python
from fredapi import Fred
import pandas as pd

fred = Fred(api_key='your_api_key')

# Get GDP data as it was known on June 1, 2014
gdp_as_of = fred.get_series_as_of_date('GDP', '6/1/2014')
print(gdp_as_of.tail())
# Output DataFrame with columns: date, realtime_start, value
#            date      realtime_start    value
# 2237  2013-10-01  2014-01-30         17102.5
# 2238  2013-10-01  2014-02-28         17080.7
# 2239  2013-10-01  2014-03-27         17089.6
# 2241  2014-01-01  2014-04-30         17149.6
# 2242  2014-01-01  2014-05-29         17101.3

# Get latest value known for each date as of a specific point in time
gdp_known = fred.get_series_as_of_date('GDP', '2020-03-15')
latest_known = gdp_known.groupby('date')['value'].last()
print(latest_known.tail())
```

--------------------------------

### Get Series First Release (Ignore Revisions) - Python

Source: https://github.com/mortada/fredapi/blob/master/README.md

Retrieves a specific economic data series from FRED, ignoring any revisions. This function returns the initial data as it was first released. The output is a pandas Series.

```python
data = fred.get_series_first_release('GDP')
data.tail()
```

--------------------------------

### Get Series First Release Data (Python)

Source: https://context7.com/mortada/fredapi/llms.txt

Retrieves only the first-release data values for a series, ignoring all subsequent revisions. This is useful for backtesting and understanding initial data reports. It requires the 'fredapi' library and an API key.

```python
from fredapi import Fred
import pandas as pd

fred = Fred(api_key='your_api_key')

# Get first-release GDP data (ignores all revisions)
gdp_first = fred.get_series_first_release('GDP')
print(gdp_first.tail())
# Output shows original values before any revisions:
# date
# 2023-01-01    26149.6
# 2023-04-01    26467.8
# 2023-07-01    27063.0
# 2023-10-01    27543.5
# Name: value, dtype: object

# Compare first release vs latest release
gdp_latest = fred.get_series_latest_release('GDP')
comparison = pd.DataFrame({
    'first_release': gdp_first,
    'latest': gdp_latest
}).tail()
print(comparison)
```

--------------------------------

### Get Series Vintage Dates (Python)

Source: https://context7.com/mortada/fredapi/llms.txt

Returns a list of all vintage dates for a series, which are the dates when data values were revised or new observations were released. Useful for point-in-time analysis. Requires 'fredapi' and an API key.

```python
from fredapi import Fred
import pandas as pd

fred = Fred(api_key='your_api_key')

# Get all vintage dates for GDP series
vintage_dates = fred.get_series_vintage_dates('GDP')
print(f"Total vintage dates: {len(vintage_dates)}")
print("Most recent vintage dates:")
for dt in vintage_dates[-5:]:
    print(dt.strftime('%Y-%m-%d'))
# Output:
# Total vintage dates: 245
# Most recent vintage dates:
# 2024-03-28
# 2024-04-25
# 2024-05-30
# 2024-06-27
# 2024-07-25

# Create a point-in-time dataset using vintage dates
pit_data = {}
for vd in vintage_dates[-12:]:  # Last 12 vintages
    data = fred.get_series_as_of_date('GDP', vd.strftime('%Y-%m-%d'))
    pit_data[vd] = data.groupby('date')['value'].last()
pit_df = pd.DataFrame(pit_data)
```

--------------------------------

### GET /series/as_of_date

Source: https://context7.com/mortada/fredapi/llms.txt

Returns data as it was known on a specific historical date, useful for point-in-time analysis.

```APIDOC
## GET /series/as_of_date

### Description
Returns the data as it was known on a specific historical date, including all revisions up to that date but ignoring any revisions made after.

### Method
GET

### Parameters
#### Query Parameters
- **series_id** (string) - Required - The FRED series ID
- **vintage_date** (string) - Required - The date (YYYY-MM-DD) to retrieve data as of

### Response
#### Success Response (200)
- **data** (DataFrame) - A DataFrame containing columns: date, realtime_start, and value.
```

--------------------------------

### Get all vintage dates for a series

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

Retrieves a list of all vintage dates associated with a specific economic data series. Vintage dates indicate when a particular data point was recorded or revised.

```python
vintage_dates = fred.get_series_vintage_dates('GDP')
```

--------------------------------

### Get latest release of a series

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

Fetches the latest available release of a specified economic data series. This is equivalent to calling the standard get_series() method.

```python
data = fred.get_series_latest_release('GDP')
```

--------------------------------

### GET /series/vintage_dates

Source: https://context7.com/mortada/fredapi/llms.txt

Returns a list of all vintage dates when data values were revised or released.

```APIDOC
## GET /series/vintage_dates

### Description
Returns a list of all vintage dates for a series, which are the dates when data values were revised or new observations were released.

### Method
GET

### Parameters
#### Query Parameters
- **series_id** (string) - Required - The FRED series ID

### Response
#### Success Response (200)
- **dates** (list) - A list of datetime objects representing vintage dates.
```

--------------------------------

### Get Series All Releases History (Python)

Source: https://context7.com/mortada/fredapi/llms.txt

Retrieves the complete revision history for a series, including all first releases and subsequent revisions. Returns a DataFrame with observation date, reporting date, and value. Requires 'fredapi' and an API key.

```python
from fredapi import Fred

fred = Fred(api_key='your_api_key')

# Get all releases and revisions for GDP
gdp_all = fred.get_series_all_releases('GDP')
print(gdp_all.tail(10))
# Output:
#       realtime_start       date     value
# 2236  2014-07-30      2013-07-01  16872.3
# 2237  2014-01-30      2013-10-01  17102.5
# 2238  2014-02-28      2013-10-01  17080.7
# 2239  2014-03-27      2013-10-01  17089.6
# 2240  2014-07-30      2013-10-01  17078.3
# 2241  2014-04-30      2014-01-01  17149.6
# 2242  2014-05-29      2014-01-01  17101.3
# 2243  2014-06-25      2014-01-01  17016.0
# 2244  2014-07-30      2014-01-01  17044.0
# 2245  2014-07-30      2014-04-01  17294.7

# Analyze revision patterns for a specific quarter
q1_2014 = gdp_all[gdp_all['date'] == '2014-01-01']
print("Revisions for Q1 2014 GDP:")
print(q1_2014)

# With custom realtime date range
gdp_recent = fred.get_series_all_releases('GDP', realtime_start='2023-01-01', realtime_end='2023-12-31')
```

--------------------------------

### Get series data as of a specific date

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

Retrieves the data for a specified series as it was known on a particular date. This method is crucial for understanding historical data states and revisions.

```python
fred.get_series_as_of_date('GDP', '6/1/2014')
```

--------------------------------

### Get Series Metadata with fredapi

Source: https://context7.com/mortada/fredapi/llms.txt

Retrieves metadata for a FRED series using get_series_info. Returns a pandas Series containing details like title, frequency, observation dates, units, and notes. Requires 'fredapi' and 'pandas'.

```Python
from fredapi import Fred

fred = Fred(api_key='your_api_key')

# Get information about the PAYEMS (payroll employment) series
info = fred.get_series_info('PAYEMS')
print(info)

# Access specific fields
print(f"Title: {info['title']}")
print(f"Frequency: {info['frequency']}")
print(f"Last Updated: {info['last_updated']}")
```

--------------------------------

### Get Series Latest Release - Python

Source: https://github.com/mortada/fredapi/blob/master/README.md

Retrieves the latest available data for a specific economic series from FRED. This is equivalent to calling the standard get_series() method. The output is a pandas Series.

```python
data = fred.get_series_latest_release('GDP')
data.tail()
```

--------------------------------

### Get all release dates for a series

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

Returns a pandas DataFrame containing all historical data releases for a specified economic series from ALFRED. This provides a comprehensive view of data revisions over time.

```python
df = fred.get_series_all_releases('GDP')
df.tail()
```

--------------------------------

### Initialize Fred API client

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

Demonstrates how to initialize the Fred API client with your API key. The API key can be provided directly, via an environment variable, or from a file.

```python
from fredapi import Fred
fred = Fred(api_key='insert api key here')
data = fred.get_series('SP500')
```

--------------------------------

### Initialize Fred Class in Python

Source: https://context7.com/mortada/fredapi/llms.txt

Demonstrates various ways to initialize the Fred class, including passing the API key directly, loading from a file, using an environment variable, and configuring proxy support. Requires the 'fredapi' library.

```Python
from fredapi import Fred

# Method 1: Pass API key directly
fred = Fred(api_key='your_api_key_here')

# Method 2: Load API key from a file
fred = Fred(api_key_file='/path/to/api_key.txt')

# Method 3: Use environment variable FRED_API_KEY
import os
os.environ['FRED_API_KEY'] = 'your_api_key_here'
fred = Fred()

# Method 4: With proxy support
fred = Fred(
    api_key='your_api_key_here',
    proxies={'http': 'http://proxy.example.com:8080', 'https': 'https://proxy.example.com:8080'}
)
```

--------------------------------

### Search for Economic Data Series by Keyword

Source: https://context7.com/mortada/fredapi/llms.txt

Demonstrates how to search for all series related to a specific keyword using the search method. Setting the limit to 0 retrieves all available results.

```python
from fredapi import Fred
fred = Fred(api_key='your_api_key')
unemployment = fred.search('unemployment rate', limit=0)
print(f"Found {len(unemployment)} unemployment-related series")
```

--------------------------------

### Retrieve Time Series Data with fredapi

Source: https://context7.com/mortada/fredapi/llms.txt

Shows how to fetch time series data for a given FRED series ID using the get_series method. Supports filtering by date range and passing additional FRED API parameters. Returns a pandas Series. Requires 'fredapi' and 'pandas'.

```Python
from fredapi import Fred

fred = Fred(api_key='your_api_key')

# Get S&P 500 data
sp500 = fred.get_series('SP500')
print(sp500.tail())

# Get GDP data with date range
gdp = fred.get_series('GDP', observation_start='2020-01-01', observation_end='2023-12-31')
print(gdp)

# Using additional FRED API parameters (e.g., units transformation)
cpi = fred.get_series('CPIAUCSL', units='pc1')  # percent change from year ago
print(cpi.tail())
```

--------------------------------

### Search by Release

Source: https://context7.com/mortada/fredapi/llms.txt

Searches for all series belonging to a specific FRED release ID. Useful for finding related data series published together.

```APIDOC
## POST /fred/release/series

### Description
Searches for all series belonging to a specific FRED release ID.

### Method
POST

### Endpoint
/fred/release/series

### Parameters
#### Path Parameters
- **release_id** (integer) - Required - The ID of the FRED release.

#### Query Parameters
- **limit** (integer) - Optional - The number of observations to return.
- **order_by** (string) - Optional - The field to order the results by (e.g., 'popularity', 'series_id').
- **sort_order** (string) - Optional - The order to sort the results ('asc' or 'desc').

### Request Example
```python
from fredapi import Fred

fred = Fred(api_key='your_api_key')

# Search for series in the Employment Situation release (release_id=50)
employment_series = fred.search_by_release(50, limit=10, order_by='popularity', sort_order='desc')
print(employment_series[['title', 'frequency', 'popularity']].head())

# Get all series from Personal Income release (release_id=175)
pi_series = fred.search_by_release(
    175,
    limit=100,
    order_by='series_id',
    sort_order='asc'
)
print(f"Found {len(pi_series)} series in release 175")
print(pi_series[['title', 'observation_start']].head())
```

### Response
#### Success Response (200)
- **Series Data** (DataFrame) - A pandas DataFrame containing the series found.

#### Response Example
```
           title                                     frequency  popularity
series id
UNRATE     Unemployment Rate                         Monthly    95
PAYEMS     All Employees: Total Nonfarm Payrolls     Monthly    86
...
Found 100 series in release 175
         title observation_start
series id
175A050A024AA   1959-01-01
175A050A024SA   1959-01-01
...
```
```

--------------------------------

### Fetch Latest Release Data with fredapi

Source: https://context7.com/mortada/fredapi/llms.txt

Uses get_series_latest_release to fetch the most recent data values for a given FRED series. Functionally equivalent to get_series() but specifically targets the latest release. Returns a pandas Series. Requires 'fredapi' and 'pandas'.

```Python
from fredapi import Fred

fred = Fred(api_key='your_api_key')

# Get latest GDP values
gdp_latest = fred.get_series_latest_release('GDP')
print(gdp_latest.tail())
```

--------------------------------

### Search by Category

Source: https://context7.com/mortada/fredapi/llms.txt

Searches for all series within a specific FRED category. Categories organize series by topic.

```APIDOC
## POST /fred/category/series

### Description
Searches for all series within a specific FRED category.

### Method
POST

### Endpoint
/fred/category/series

### Parameters
#### Path Parameters
- **category_id** (integer) - Required - The ID of the FRED category.

#### Query Parameters
- **limit** (integer) - Optional - The number of observations to return.
- **order_by** (string) - Optional - The field to order the results by (e.g., 'popularity', 'series_id').
- **sort_order** (string) - Optional - The order to sort the results ('asc' or 'desc').
- **filter** (tuple) - Optional - A tuple to filter results (e.g., ('seasonal_adjustment', 'Seasonally Adjusted')).

### Request Example
```python
from fredapi import Fred

fred = Fred(api_key='your_api_key')

# Search for series in category 32991 (H.15 Selected Interest Rates)
interest_rates = fred.search_by_category(
    32991,
    limit=20,
    order_by='popularity',
    sort_order='desc'
)
print(interest_rates[['title', 'frequency', 'popularity']].head())

# Get series from Money Supply category (category_id=32242)
money_supply = fred.search_by_category(
    32242,
    filter=('seasonal_adjustment', 'Seasonally Adjusted')
)
print(money_supply[['title', 'units']].head(10))
```

### Response
#### Success Response (200)
- **Series Data** (DataFrame) - A pandas DataFrame containing the series found.

#### Response Example
```
           title                                    frequency  popularity
series id
DFF        Federal Funds Effective Rate            Daily       88
FEDFUNDS   Federal Funds Effective Rate            Monthly     85
...
         title units
series id
BOGMBASE   Billions of Persons, Seasonally Adjusted
M1         Billions of Dollars, Seasonally Adjusted
...
```
```

--------------------------------

### Search for series by release or category ID

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

Allows programmatic searching of FRED data series using release IDs or category IDs. Supports options for limiting results, ordering, and sorting.

```python
df1 = fred.search_by_release(11)
df2 = fred.search_by_category(101, limit=10, order_by='popularity', sort_order='desc')
```

--------------------------------

### Retrieve vintage dates for a series

Source: https://github.com/mortada/fredapi/blob/master/README.md

Retrieves a list of all vintage dates available for a specific series. The output is a list of datetime objects, which can be formatted as strings for display.

```python
from __future__ import print_function
vintage_dates = fred.get_series_vintage_dates('GDP')
for dt in vintage_dates[-5:]:
    print(dt.strftime('%Y-%m-%d'))
```

--------------------------------

### Search for economic data series by keyword

Source: https://github.com/mortada/fredapi/blob/master/DESCRIPTION.rst

Performs a full-text search for economic data series based on a keyword query. Returns a pandas DataFrame with matching series information.

```python
fred.search('potential gdp')
```

--------------------------------

### Search for Series by Release ID

Source: https://context7.com/mortada/fredapi/llms.txt

Retrieves economic series associated with a specific FRED release ID. This is useful for grouping data published in the same report, such as employment or personal income data.

```python
from fredapi import Fred
fred = Fred(api_key='your_api_key')
# Search for series in the Employment Situation release
employment_series = fred.search_by_release(50, limit=10, order_by='popularity', sort_order='desc')
print(employment_series[['title', 'frequency', 'popularity']].head())
# Get all series from Personal Income release
pi_series = fred.search_by_release(175, limit=100, order_by='series_id', sort_order='asc')
print(f"Found {len(pi_series)} series in release 175")
```

--------------------------------

### Search for Series

Source: https://context7.com/mortada/fredapi/llms.txt

Searches for series based on a keyword. The `limit=0` parameter retrieves all matching series without any restriction.

```APIDOC
## POST /fred/series/search

### Description
Searches for all unemployment-related series (no limit).

### Method
POST

### Endpoint
/fred/series/search

### Parameters
#### Query Parameters
- **search_text** (string) - Required - The text to search for.
- **limit** (integer) - Optional - The number of observations to return.

### Request Example
```python
from fredapi import Fred

fred = Fred(api_key='your_api_key')

unemployment = fred.search('unemployment rate', limit=0)
print(f"Found {len(unemployment)} unemployment-related series")
```

### Response
#### Success Response (200)
- **Series Data** (DataFrame) - A pandas DataFrame containing the series found.

#### Response Example
```
Found 150 unemployment-related series
```
```

--------------------------------

### Search FRED Series (Python)

Source: https://context7.com/mortada/fredapi/llms.txt

Performs a full-text search across all FRED series, returning a DataFrame with information like title, frequency, units, and popularity. Supports sorting, filtering, and limiting results. Requires 'fredapi' and an API key.

```python
from fredapi import Fred

fred = Fred(api_key='your_api_key')

# Basic search for GDP-related series
results = fred.search('potential gdp')
print(results[['title', 'frequency', 'popularity']].head())
# Output:
#            title                                   frequency  popularity
# series id
# GDPPOT     Real Potential Gross Domestic Product  Quarterly  72
# NGDPPOT    Nominal Potential Gross Domestic ...   Quarterly  61

# Search with options
inflation_series = fred.search(
    'consumer price index',
    limit=10,
    order_by='popularity',
    sort_order='desc',
    filter=('frequency', 'Monthly')
)
print(inflation_series[['title', 'popularity', 'seasonal_adjustment']].head())
```

--------------------------------

### Search for economic data series

Source: https://github.com/mortada/fredapi/blob/master/README.md

Performs a full-text search across the FRED database to find relevant series IDs. The results are returned as a pandas DataFrame, which can be transposed for easier readability.

```python
fred.search('potential gdp').T
```

--------------------------------

### Search for Series by Category ID

Source: https://context7.com/mortada/fredapi/llms.txt

Searches for economic series within a specific FRED category. Supports filtering by attributes such as seasonal adjustment.

```python
from fredapi import Fred
fred = Fred(api_key='your_api_key')
# Search for series in category 32991 (H.15 Selected Interest Rates)
interest_rates = fred.search_by_category(32991, limit=20, order_by='popularity', sort_order='desc')
print(interest_rates[['title', 'frequency', 'popularity']].head())
# Get series from Money Supply category with filter
money_supply = fred.search_by_category(32242, filter=('seasonal_adjustment', 'Seasonally Adjusted'))
print(money_supply[['title', 'units']].head(10))
```

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