def test_example(get_data_file, test_output_dir):
file_name = get_data_file(
"meteorites.csv",
"https://data.nasa.gov/api/views/gh4g-9sfh/rows.csv?accessType=DOWNLOAD",
)
df = pd.read_csv(file_name)
# Note: Pandas does not support dates before 1880, so we ignore these for this analysis
df["year"] = pd.to_datetime(df["year"], errors="coerce")
# Example: Constant variable
df["source"] = "NASA"
# Example: Boolean variable
df["boolean"] = np.random.choice([True, False], df.shape[0])
# Example: Mixed with base types
df["mixed"] = np.random.choice([1, "A"], df.shape[0])
# Example: Highly correlated variables
df["reclat_city"] = df["reclat"] + np.random.normal(scale=5,
size=(len(df)))
# Example: Duplicate observations
duplicates_to_add = pd.DataFrame(df.iloc[0:10])
duplicates_to_add["name"] += " copy"
df = df.append(duplicates_to_add, ignore_index=True)
output_file = test_output_dir / "profile.html"
profile = ProfileReport(df,
title="NASA Meteorites",
samples={
"head": 5,
"tail": 5
},
sort="ascending")
profile.to_file(output_file=output_file)
assert (test_output_dir /
"profile.html").exists(), "Output file does not exist"
assert (type(profile.get_description()) == dict and len(
profile.get_description().items()) == 7), "Unexpected result"
if sys.version_info[1] >= 6:
assert list(profile.get_description()["variables"].keys()) == [
"boolean",
"fall",
"GeoLocation",
"id",
"mass (g)",
"mixed",
"name",
"nametype",
"recclass",
"reclat",
"reclat_city",
"reclong",
"source",
"year",
], "Ascending sort did not work"
def test_issue353():
df = pd.DataFrame(np.random.rand(100, 5),
columns=["a", "b", "c", "d", "e"])
# make one column categorical
df["a"] = df["a"].multiply(5).astype("int").astype("category")
profile = ProfileReport(df,
title="Pandas Profiling Report",
html={"style": {
"full_width": True
}})
profile.get_description()
def test_example(get_data_file, test_output_dir):
file_name = get_data_file(
"meteorites.csv",
"https://data.nasa.gov/api/views/gh4g-9sfh/rows.csv?accessType=DOWNLOAD",
)
# For reproducibility
np.random.seed(7331)
df = pd.read_csv(file_name)
# Note: Pandas does not support dates before 1880, so we ignore these for this analysis
df["year"] = pd.to_datetime(df["year"], errors="coerce")
# Example: Constant variable
df["source"] = "NASA"
# Example: Boolean variable
df["boolean"] = np.random.choice([True, False], df.shape[0])
# Example: Mixed with base types
df["mixed"] = np.random.choice([1, "A"], df.shape[0])
# Example: Highly correlated variables
df["reclat_city"] = df["reclat"] + np.random.normal(scale=5,
size=(len(df)))
# Example: Duplicate observations
duplicates_to_add = pd.DataFrame(df.iloc[0:10].copy())
df = df.append(duplicates_to_add, ignore_index=True)
output_file = test_output_dir / "profile.html"
profile = ProfileReport(
df,
title="NASA Meteorites",
samples={
"head": 5,
"tail": 5
},
duplicates={"head": 10},
minimal=True,
)
profile.to_file(output_file)
assert (test_output_dir /
"profile.html").exists(), "Output file does not exist"
assert (type(profile.get_description()) == dict and len(
profile.get_description().items()) == 10), "Unexpected result"
assert "<span class=badge>12</span>" in profile.to_html()
def test_issue85():
data = {
"booleans_type": [False, True, True],
"booleans_type_nan": [False, True, np.nan],
"integers": [1, 0, 0],
"integers_nan": [1, 0, np.nan],
"str_yes_no": ["Y", "N", "Y"],
"str_yes_no_mixed": ["Y", "n", "y"],
"str_yes_no_nana": ["Y", "N", np.nan],
"str_true_false": ["True", "False", "False"],
"str_true_false_nan": ["True", "False", np.nan],
}
df = pd.DataFrame(data)
report = ProfileReport(
df,
pool_size=1,
title="Dataset with <em>Boolean</em> Variables",
samples={"head": 20},
)
for col, variable_stats in report.get_description()["variables"].items():
assert (
variable_stats["type"] == Variable.TYPE_BOOL
), "Variable should be boolean"
def test_check_date_type_warning():
df = pd.DataFrame(["2018-01-01", "2017-02-01", "2018-04-07"],
columns=["date"])
report = ProfileReport(df)
assert any(message.message_type == MessageType.TYPE_DATE
for message in report.get_description()
["messages"]), "Date warning should be present"
def test_issue51_empty():
df = pd.DataFrame({
"test": ["", "", "", "", ""],
"blest": ["", "", "", "", ""],
"bert": ["", "", "", "", ""],
})
report = ProfileReport(
df,
title="Pandas Profiling Report",
progress_bar=False,
explorative=True,
)
report.config.vars.num.low_categorical_threshold = 0
assert ("cramers" not in report.get_description()["correlations"]
or (report.get_description()["correlations"]["cramers"].values
== np.ones((3, 3))).all())
def pandas_profiling_test(df):
from pandas_profiling import ProfileReport
report = ProfileReport(df, minimal=True)
description = report.get_description()
table = description["table"]
return table
def test_issue377(df):
if df is None:
pytest.skip("dataset unavailable")
return
original_order = tuple(df.columns.values)
profile = ProfileReport(df, sort=None, pool_size=1, progress_bar=False)
new_order = tuple(profile.get_description()["variables"].keys())
assert original_order == new_order
def test_example_empty():
df = pd.DataFrame({"A": [], "B": []})
profile = ProfileReport(df)
description = profile.get_description()
assert len(description["correlations"]) == 0
assert len(description["missing"]) == 0
assert len(description["sample"]) == 0
html = profile.to_html()
assert "Dataset is empty" in html
def test_custom_sample():
df = pd.DataFrame({"test": [1, 2, 3, 4, 5]})
# In case that a sample of the real data (cars) would disclose sensitive information, we can replace it with
# mock data. For illustrative purposes, we use data based on cars from a popular game series.
mock_data = pd.DataFrame({
"make": ["Blista Kanjo", "Sentinel", "Burrito"],
"price": [58000, 95000, 65000],
"mpg": [20, 30, 22],
"rep78": ["Average", "Excellent", "Fair"],
"headroom": [2.5, 3.0, 1.5],
"trunk": [8, 10, 4],
"weight": [1050, 1600, 2500],
"length": [165, 170, 180],
"turn": [40, 50, 32],
"displacement": [80, 100, 60],
"gear_ratio": [2.74, 3.51, 2.41],
"foreign": ["Domestic", "Domestic", "Foreign"],
})
# Length left out due to correlation with weight.
report = ProfileReport(
df,
title="Test custom sample",
sample={
"name":
"Mock data sample",
"data":
mock_data,
"caption":
"Disclaimer: this is synthetic data generated based on the format of the data in this table.",
},
minimal=True,
)
samples = report.get_description()["sample"]
assert len(samples) == 1
sample = samples[0]
assert sample.id == "custom"
assert hash_dataframe(sample.data) == hash_dataframe(mock_data)
assert sample.name == "Mock data sample"
assert (
sample.caption ==
"Disclaimer: this is synthetic data generated based on the format of the data in this table."
)
html = report.to_html()
assert "Mock data sample" in html
assert all(make in html for make in mock_data["make"].values.tolist())
assert (
"Disclaimer: this is synthetic data generated based on the format of the data in this table"
in html)
def test_issue523():
# https://github.com/pandas-dev/pandas/issues/33803
data = [
1871248,
12522551,
1489260,
6657093,
pd.NA,
pd.NA,
pd.NA,
pd.NA,
pd.NA,
1489260,
pd.NA,
2468576,
]
df = pd.DataFrame({"col": data}, dtype=pd.Int64Dtype())
profile_report = ProfileReport(df, title="Test Report", progress_bar=False)
assert len(profile_report.get_description()) > 0
def test_interactions_target():
n_rows = 10
n_columns = 50
n_targets = 2
df = pd.DataFrame(
np.random.randint(0, 1000, size=(n_rows, n_columns)),
columns=[f"column_{c}" for c in range(n_columns)],
)
targets = [f"column_{target}" for target in range(0, n_targets)]
profile = ProfileReport(df,
minimal=True,
interactions={
"continuous": True,
"targets": targets
})
total = sum(
len(v.keys()) for k, v in profile.get_description()["scatter"].items())
assert total == n_targets * n_columns
def test_modular_description_set(tdf):
profile = ProfileReport(
tdf,
title="Modular test",
duplicates=None,
samples={
"head": 0,
"tail": 0
},
correlations=None,
interactions=None,
missing_diagrams={
"matrix": False,
"bar": False,
"dendrogram": False,
"heatmap": False,
},
pool_size=1,
)
html = profile.get_description()
assert len(html) > 0
def test_issue351():
data = pd.DataFrame(["Jan", 1]).set_index(0)
profile = ProfileReport(data)
assert (profile.get_description()["variables"]["0"]["type"] ==
Variable.S_TYPE_UNSUPPORTED)
for k, df in datasets.items():
print("------------")
print(f"TRAINING DATASET ({k.upper()})")
print("------------")
profile = ProfileReport(df, title=f"Passengers Training Data ({k.title()})", html={"style":{"full_width":True}})
print(type(profile))
if TO_HTML:
profile_path = os.path.join(REPORTS_DIR, f"passengers_profile_{k}.html")
profile.to_file(output_file=profile_path)
#webbrowser.open(os.path.abspath(TRAINING_PROFILE_FILEPATH))
#> RESULTS ...
desc = profile.get_description()
#print(desc.keys()) #> ['table', 'variables', 'scatter', 'correlations', 'missing', 'messages', 'package']
print("------------")
print("MESSAGES:")
print("------------")
for message in desc["messages"]:
print(message)
#print("------------")
#print("VARS:")
#print("------------")
#for k,v in desc["variables"].items():
# print(k.upper())
# print(v.keys())
# #pprint(v)
# print("---")
def test_issue282():
index = [
"BJ110",
"BJ126",
"BJ163",
"BJ054",
"BJ017",
"LP045",
"BJ153",
"AD013",
"NL047",
"BJ036",
"AD026",
"BJ018",
"LP044",
"LP006",
"BO014",
"BJ035",
"BJ155",
"TLL003",
"BJ073",
"BJ068",
"BJ049",
"TLL061",
"NL010",
"AD019",
"LP003",
"BJ107",
"BJ023",
"BJ012",
"TLL067",
"LP020",
"AD031",
"BJ172",
"NL031",
"LP032",
"AD016",
"BJ077",
"BJ047",
"BJ001",
"BJ105",
"BJ062",
"AD022",
"BJ106",
"BJ102",
"BJ022",
"BJ010",
"TLL007",
"AD011",
"LP018",
"TLL004",
"TLL030",
"BJ005",
"AD003",
"BJ025",
"LP005",
"BJ144",
"BJ080",
"TLL062",
"BJ166",
"LP014",
"NL005",
"TLL038",
"BJ072",
"AD032",
"BO001",
"BO024",
"BO005",
"AD004",
"TLL006",
"BJ063",
"BJ007",
"LP007",
"BJ159",
"NL056",
"NL059",
"BJ115",
"NL037",
"BJ003",
"BJ117",
"AD025",
"BJ050",
"LP029",
"BJ149",
"AD002",
"AD010",
"BJ160",
"BJ147",
"BO023",
"NL055",
"NL038",
"BO004",
"BJ123",
"NL051",
"NL011",
]
df = pd.DataFrame(
index=index,
data={"column_1": ["value"] * len(index), "column_2": [1.0] * len(index)},
)
report = ProfileReport(df)
description = report.get_description()
assert type(description) == dict
def test_issue351():
data = pd.DataFrame(["Jan", 1]).set_index(0)
profile = ProfileReport(data, progress_bar=False)
assert profile.get_description()["variables"]["0"]["type"] == "Unsupported"
