Update README.md

README.md

@@ -47,11 +47,12 @@ It is as easy as:
 from datasets import load_dataset
 import polars as pl
 
-# This line will download 6.6GB+ of RFSD data and store it in a cache folder
+# This line will download 6.6GB+ of all RFSD data and store it in a 🤗 cache folder
 RFSD = load_dataset('irlspbru/RFSD')
 
-# Alternatively, one can read Hugging Face-hosted Parquet files directly into Polars
-RFSD = pl.read_parquet('hf://datasets/irlspbru/RFSD/*.parquet')
+# Alternatively, this will download ~540MB with all financial statements for 2023
+# to a Polars DataFrame (requires about 8GB or RAM)
+RFSD_2023 = pl.read_parquet('hf://datasets/irlspbru/RFSD/RFSD/year=2023/*.parquet')
 ```
 
 We provide a file in `aux/descriptive_names_dict.csv` in [GitHub repository](https://github.com/irlcode/RFSD) which can be used to change the original names of financial variables to user-friendly ones, e.g. `B_revenue` and `CFo_materials` in lieu of `line_2110` and `line_4121`, respectively. Prefixes are for disambiguation purposes: `B_` stands for balance sheet variables, `PL_` — profit and loss statement, `CFi_` and `CFo` — cash inflows and cash outflows, etc. (One can find all the variable definitions in the supplementary materials table in the accompanying paper and [consult](https://www.consultant.ru/document/cons_doc_LAW_32453/) the original statement forms used by firms: full is `KND 0710099`, simplified — `KND 0710096`.)
@@ -62,21 +63,6 @@ renaming_df = pl.read_csv('https://raw.githubusercontent.com/irlcode/RFSD/main/a
 RFSD = RFSD.rename({item[0]: item[1] for item in zip(renaming_df['original'], renaming_df['descriptive'])})
 ```
 
-You can also query the dataset in a streaming fashion to download and import only a subset of rows or columns into memory: 
-``` Python
-from datasets import load_dataset
-import polars as pl
-
-# Load dataset in streaming mode
-RFSD_stream = load_dataset("irlspbru/RFSD", streaming = True)
-
-# Load only 2019 data into memory
-RFSD_2019 = pl.DataFrame([row for row in RFSD_stream if row['year'] == 2019])
-
-# Load only revenue for firms in 2019, identified by taxpayer id
-RFSD_2019_revenue = pl.DataFrame([row for row in RFSD_stream.select_columns(['inn', 'line_2110']) if row['year'] == 2019])
-```
-
 Please note that the data is not shuffled within year, meaning that streaming first __n__ rows will not yield a random sample. 
 
 ### R