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Package to calculate economic complexity and associated variables

Project description

# Economic Complexity and Product Complexity

Python package to calculate economic complexity indices.

STATA implementation of the economic complexity index available at: https://github.com/cid-harvard/ecomplexity

Explore complexity and associated data using Harvard CID's Atlas tool: http://atlas.cid.harvard.edu

### Tutorial

**Installation**:
At terminal: `pip install ecomplexity`

**Usage**:
```python
from ecomplexity import ecomplexity
from ecomplexity import proximity

# Import trade data from CID Atlas
data_url = "https://intl-atlas-downloads.s3.amazonaws.com/country_hsproduct2digit_year.csv.zip"
data = pd.read_csv(data_url, compression="zip", low_memory=False)
data = data[['year','location_code','hs_product_code','export_value']]

# Calculate complexity
trade_cols = {'time':'year', 'loc':'location_code', 'prod':'hs_product_code', 'val':'export_value'}
cdata = ecomplexity(data, trade_cols)

# Calculate proximity matrix
prox_df = proximity(data, trade_cols)
```
**Arguments**:
```
data: pandas dataframe containing production / trade data.
Including variables indicating time, location, product and value
cols_input: dict of column names for time, location, product and value.
Example: {'time':'year', 'loc':'origin', 'prod':'hs92', 'val':'export_val'}
presence_test: str for test used for presence of industry in location.
One of "rca" (default), "rpop", "both", or "manual".
Determines which values are used for M_cp calculations.
If "manual", M_cp is taken as given from the "value" column in data
val_errors_flag: {'coerce','ignore','raise'}. Passed to pd.to_numeric
*default* coerce.
rca_mcp_threshold: numeric indicating RCA threshold beyond which mcp is 1.
*default* 1.
rpop_mcp_threshold: numeric indicating RPOP threshold beyond which mcp is 1.
*default* 1. Only used if presence_test is not "rca".
pop: pandas df, with time, location and corresponding population, in that order.
Not required if presence_test is "rca" (default).
continuous: Used to calculate product proximities, indicates whether
to consider correlation of every product pair (True) or product
co-occurrence (False). *default* False.
asymmetric: Used to calculate product proximities, indicates whether
to generate asymmetric proximity matrix (True) or symmetric (False).
*default* False.
```

### TODO:

- There are very minor differences in the values of density, COI and COG between STATA and Python due to the way matrix computations are handled by the two. These should be aligned in the future.
- knn options for density: in the future, allow knn parameter for density calculation

### References:
- Hausmann, R., Hidalgo, C. A., Bustos, S., Coscia, M., Simoes, A., & Yıldırım, M. (2013). The Atlas of Economic Complexity: Mapping Paths to Prosperity (Part 1). Retrieved from https://growthlab.cid.harvard.edu/files/growthlab/files/atlas_2013_part1.pdf
- Hidalgo, C. A., Klinger, B., Barabasi, A.-L., & Hausmann, R. (2007). The Product Space Conditions the Development of Nations. Science, 317(5837), 482–487. http://doi.org/10.1126/science.1144581


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