Datasets

List all datasets

import ravix
ravix.get_data()

Available data files in Ravix (v1.0.1):
  - airbnb.csv
  - betas.csv
  - charges.csv
  - countries.csv
  - employment.csv
  - facebook.csv
  - house_prices.csv
  - hr_retention.csv
  - insurance.csv
  - job_changes.csv
  - loan_default.csv
  - marketing_toys.csv
  - price_size.csv
  - salary_churn.csv
  - sales.csv
  - sales_ads.csv
  - tire_sales.csv
  - top200.csv
  - tweet_engagement.csv
  - vacation.csv
  - video_engagement.csv

Load and inspect

df = ravix.get_data("Betas.csv")
df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 36 entries, 0 to 35
Data columns (total 9 columns):
 #   Column      Non-Null Count  Dtype  
---  ------      --------------  -----  
 0   Unnamed: 0  36 non-null     object 
 1   AAPL        36 non-null     float64
 2   CAT         36 non-null     float64
 3   JNJ         36 non-null     float64
 4   MCD         36 non-null     float64
 5   PG          36 non-null     float64
 6   MSFT        36 non-null     float64
 7   XOM         36 non-null     float64
 8   SPY         36 non-null     float64
dtypes: float64(8), object(1)
memory usage: 2.7+ KB

df.describe()

            AAPL        CAT        JNJ  ...       MSFT        XOM        SPY
count  36.000000  36.000000  36.000000  ...  36.000000  36.000000  36.000000
mean    0.032999   0.017261   0.010656  ...   0.030299  -0.000624   0.015307
std     0.095151   0.082262   0.053878  ...   0.053613   0.102811   0.054134
min    -0.184045  -0.204407  -0.115917  ...  -0.080090  -0.251261  -0.129987
25%    -0.035885  -0.037298  -0.019163  ...   0.005981  -0.059978  -0.000434
50%     0.055795   0.039862   0.020563  ...   0.034708   0.000158   0.022039
75%     0.101165   0.061480   0.041866  ...   0.062072   0.072237   0.042849
max     0.214380   0.186986   0.144208  ...   0.136326   0.223861   0.133610

[8 rows x 8 columns]

Betas.csv — column reference