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adding docs
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@ -1,15 +1,27 @@
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use polars::prelude::*;
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use polars::prelude::*;
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const QDF_IDX_COLUMNS: [&str; 3] = ["real_date", "cid", "xcat"];
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/// The standard metrics provided by JPMaQS (`value`, `grading`, `eop_lag`, `mop_lag`).
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pub const DEFAULT_JPMAQS_METRICS: [&str; 4] = ["value", "grading", "eop_lag", "mop_lag"];
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/// The required columns for a Quantamental DataFrame.
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pub const QDF_INDEX_COLUMNS: [&str; 3] = ["real_date", "cid", "xcat"];
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/// Check if a DataFrame is a quantamental DataFrame.
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/// Check if a DataFrame is a quantamental DataFrame.
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/// A standard Quantamental DataFrame has the following columns:
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/// - `real_date`: Date column as a date type
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/// - `cid`: Column of cross-sectional identifiers
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/// - `xcat`: Column of extended categories
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///
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/// Additionally, the DataFrame should have atleast 1 more column.
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/// Typically, this is one (or more) of the default JPMaQS metics.
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pub fn is_quantamental_dataframe(df: &DataFrame) -> bool {
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pub fn is_quantamental_dataframe(df: &DataFrame) -> bool {
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let columns = df
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let columns = df
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.get_column_names()
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.get_column_names()
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.iter()
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.iter()
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.map(|s| s.as_str())
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.map(|s| s.as_str())
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.collect::<Vec<&str>>();
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.collect::<Vec<&str>>();
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let has_idx_columns = QDF_IDX_COLUMNS.iter().all(|col| columns.contains(col));
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let has_idx_columns = QDF_INDEX_COLUMNS.iter().all(|col| columns.contains(col));
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if !has_idx_columns {
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if !has_idx_columns {
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return false;
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return false;
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}
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}
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