def test_df_dtypes(csvfile):
df = _io.df_dtypes(_io.csv_sample(csvfile))
# pd.Series -> pd.DataFrame
assert isinstance(df, pd.DataFrame)
# column name, dtype
assert len(df.columns) == 2
assert tuple(df.columns) == ("columns", "types")
def test_df_fix_schema(csvfile, flags):
df = csv_sample(csvfile)
flags, flagged = set_flags(flags, df.columns)
# set flags to dtypes
flags.update((col, "string") for col in flagged)
df_t = df_fix_schema(df, flags).dtypes
assert df_t[flagged].astype(str).to_list() == ["string"] * len(flagged)
def test_df_schema_flags(csvfile, flags):
df = csv_sample(csvfile)
res = df_schema_flags(df, flags)
assert glom(res, ["datatype"]) == ["yesno"] * len(res)
# NOTE: index 0 relies on deterinistic dictionary order
assert glom(res, [(tuple, "0")]) == df.columns.to_list()
assert glom(res, [(T.values(), tuple, "0")]) == [
_FLAG_FMT.format(name=flags.instanceName, col=col)
for col in df.columns
]
def test_df_schema_dtype(csvfile, flags):
df = csv_sample(csvfile)
flags, flagged_cols = set_flags(flags, df.columns)
res = df_schema_dtype(df, flags)
assert glom(res, ["choices"]) == [_TYPES] * len(res)
# NOTE: index 0 relies on deterinistic dictionary order
assert glom(res, [(tuple, "0")]) == flagged_cols
assert glom(res, [(T.values(), tuple, "0")]) == [
_FLAG_FMT.format(name=flags.instanceName, col=col)
for col in flagged_cols
]
def test_df_schema_flags_prompt(csvfile, flags):
df = csv_sample(csvfile)
hdr, hline, *txttbl = df_schema_flags_prompt(df, flags).splitlines()
assert re.compile(("-+\\|" * 3)[:-2]).match(hline)
col_sep = " +\\| +"
cols_hdr = ["Columns", "Types", "Edit \\(Y/N\\)"]
assert re.compile(col_sep.join(cols_hdr)).match(hdr.strip())
ESCAPED_FMT = _FIELD_FMT.format(_FLAG_FMT).replace("[", "\\[")
fields = [
ESCAPED_FMT.format(name=flags.instanceName, col=col)
for col in df.columns
]
tbl_cells = zip(df.columns, map(str, df.dtypes), fields)
assert all(
re.compile(col_sep.join(cells)).match(row.strip())
for cells, row in zip(tbl_cells, txttbl))
def test_dfT_markdown(csvfile):
df = _io.csv_sample(csvfile)
txttbl = _io.dfT_markdown(df)
# check: transposed, php format, no index
assert check_md_flavour(txttbl, df.T, flavour="php")
def test_df_markdown(csvfile):
df = _io.csv_sample(csvfile)
txttbl = _io.df_markdown(df)
# check: presto format, no index
assert check_md_flavour(txttbl, df, flavour="php")
def test_csv_dtypes(csvfile):
txttbl = _io.csv_dtypes(csvfile)
ref_df = _io.csv_sample(csvfile)
assert compare_cols_transposed(txttbl, ref_df)
def test_csv_preview(csvfile):
txttbl = _io.csv_preview(csvfile, nrows=5)
ref_df = _io.csv_sample(csvfile, nrows=5)
assert compare_cols_transposed(txttbl, ref_df)
def test_csv_sample(csvfile):
df = _io.csv_sample(csvfile, nrows=3)
assert isinstance(df, pd.DataFrame)
assert len(df) == 3 # nrows