import janitor
import matplotlib.pyplot as plt
import missingno
import numpy as np
import pandas as pd
import pyreadr
import seaborn as sns
import session_info
import upsetplot
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%matplotlib inline
sns.set(
rc={
"figure.figsize": (10, 10)
}
)
sns.set_style("whitegrid")
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print(
None or True,
None or False,
None == None,
None is None,
# None + True,
# None / False,
type(None),
sep="\n"
)
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print(
np.nan or True,
np.nan or False,
np.nan == np.nan,
np.nan is np.nan,
np.nan / 2,
np.nan * 7,
type(np.nan),
np.isnan(np.nan),
sep="\n"
)
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test_missing_df = pd.DataFrame.from_dict(
data=dict(
x=[0, 1, np.nan, np.nan, None],
y=[0, 1, pd.NA, np.nan, None]
)
)
test_missing_df
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test_missing_df.isna()
test_missing_df.isnull()
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test_missing_df.x.isnull()
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pd.Series([1, np.nan])
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pd.Series([pd.to_datetime("2022-01-01"), np.nan])
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pd.Series([-1]).isnull()
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pima_indians_diabetes_url = "https://nrvis.com/data/mldata/pima-indians-diabetes.csv"
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!wget -O ./data/pima-indians-diabetes.csv { pima_indians_diabetes_url } -q
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diabetes_df = pd.read_csv(
filepath_or_buffer="./data/pima-indians-diabetes.csv", # or pima_indians_diabetes_url
sep=",",
names=[
"pregnancies",
"glucose",
"blood_pressure",
"skin_thickness",
"insulin",
"bmi",
"diabetes_pedigree_function",
"age",
"outcome",
]
)
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base_url = "https://github.com/njtierney/naniar/raw/master/data/"
datasets_names = ("oceanbuoys", "pedestrian", "riskfactors")
extension = ".rda"
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datasets_dfs = {}
for dataset_name in datasets_names:
dataset_file = f"{ dataset_name }{ extension }"
dataset_output_file = f"./data/{ dataset_file }"
dataset_url = f"{ base_url }{ dataset_file }"
!wget -q -O { dataset_output_file } { dataset_url }
datasets_dfs[f"{ dataset_name }_df"] = pyreadr.read_r(dataset_output_file).get(dataset_name)
datasets_dfs.keys()
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locals().update(**datasets_dfs)
del datasets_dfs
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oceanbuoys_df.shape, pedestrian_df.shape, riskfactors_df.shape, diabetes_df.shape
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riskfactors_df.info()
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riskfactors_df.isna()
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riskfactors_df.shape
riskfactors_df.missing.number_complete()
riskfactors_df.missing.number_missing()
riskfactors_df.missing.missing_variable_summary()
riskfactors_df.missing.missing_variable_table()
riskfactors_df.missing.missing_case_summary()
riskfactors_df.missing.missing_case_table()
(
riskfactors_df
.missing
.missing_variable_span(
variable="weight_lbs",
span_every=50
)
)
(
riskfactors_df
.missing
.missing_variable_run(
variable="weight_lbs"
)
)
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riskfactors_df.missing.number_complete()
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riskfactors_df.missing.number_missing()
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riskfactors_df.missing.missing_variable_summary()
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riskfactors_df.missing.missing_variable_table()
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riskfactors_df.missing.missing_case_summary()
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riskfactors_df.missing.missing_case_table()
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(
riskfactors_df
.missing
.missing_variable_span(
variable="weight_lbs",
span_every=50
)
)
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(
riskfactors_df
.missing
.missing_variable_run(
variable="weight_lbs"
)
)
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riskfactors_df.missing.missing_variable_plot()
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riskfactors_df.missing.missing_case_plot()
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(
riskfactors_df
.missing
.missing_variable_span_plot(
variable="weight_lbs",
span_every=10,
rot=0
)
)
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missingno.bar(df = riskfactors_df)
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missingno.matrix(df=riskfactors_df)
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(
riskfactors_df
.missing
.missing_upsetplot(
variables = None,
element_size = 60
)
)
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common_na_strings = (
"missing",
"NA",
"N A",
"N/A",
"#N/A",
"NA ",
" NA",
"N /A",
"N / A",
" N / A",
"N / A ",
"na",
"n a",
"n/a",
"na ",
" na",
"n /a",
"n / a",
" a / a",
"n / a ",
"NULL",
"null",
"",
"?",
"*",
".",
)
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common_na_numbers = (-9, -99, -999, -9999, 9999, 66, 77, 88, -1)
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missing_data_example_df = pd.DataFrame.from_dict(
dict(
x = [1, 3, "NA", -99, -98, -99],
y = ["A", "N/A", "NA", "E", "F", "G"],
z = [-100, -99, -98, -101, -1, -1]
)
)
missing_data_example_df
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missing_data_example_df.missing.number_missing()
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missing_data_example_df.dtypes
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missing_data_example_df.x.unique()
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(
missing_data_example_df
.select_dtypes(object)
.apply(pd.unique)
)
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pd.read_csv(
"./data/missing_data_enconding_example.csv",
na_filter=True,
na_values=[-99, -1]
)
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(
missing_data_example_df
.replace(
to_replace=[-99, "NA"],
value=np.nan
)
)
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(
missing_data_example_df
.replace(
to_replace={
"x": {
-99: np.nan
}
}
)
)
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implicit_to_explicit_df = pd.DataFrame.from_dict(
data={
"name": ["lynn", "lynn", "lynn", "zelda"],
"time": ["morning", "afternoon", "night", "morning"],
"value": [350, 310, np.nan, 320]
}
)
implicit_to_explicit_df
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(
implicit_to_explicit_df
.pivot_wider(
index="name",
names_from="time",
values_from="value"
)
)
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(
implicit_to_explicit_df
.value_counts(
subset=["name"]
)
.reset_index(name="n")
.query("n < 2")
)
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(
implicit_to_explicit_df
.complete(
"name",
"time",
)
)
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(
implicit_to_explicit_df
# pyjanitor
.complete(
{"name": ["lynn", "zelda"]},
{"time": ["morning", "afternoon"]},
sort=True
)
)
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(
implicit_to_explicit_df
# pyjanitor
.complete(
"name",
"time",
fill_value=np.nan
)
)
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(
implicit_to_explicit_df
# pyjanitor
.complete(
"name",
"time",
fill_value=0,
explicit=False
)
)
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diabetes_df.missing.missing_variable_plot()
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diabetes_df[diabetes_df.columns[1:6]] = diabetes_df[diabetes_df.columns[1:6]].replace(0, np.nan)
diabetes_df.missing.missing_variable_plot()
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(
diabetes_df
.missing.sort_variables_by_missingness()
.pipe(missingno.matrix)
)
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(
diabetes_df
.missing.sort_variables_by_missingness()
.sort_values(by = "blood_pressure")
.pipe(missingno.matrix)
)
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(
diabetes_df
.missing.sort_variables_by_missingness()
.sort_values("insulin")
.pipe(missingno.matrix)
)
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(
riskfactors_df
.isna()
.replace({
False: "Not missing",
True: "Missing"
})
.add_suffix("_NA")
.pipe(
lambda shadow_matrix: pd.concat(
[riskfactors_df, shadow_matrix],
axis="columns"
)
)
)
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(
riskfactors_df
.missing
.bind_shadow_matrix(only_missing = True)
)
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(
riskfactors_df
.missing
.bind_shadow_matrix(only_missing=True)
.groupby(["weight_lbs_NA"])
["age"]
.describe()
.reset_index()
)
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(
riskfactors_df
.missing
.bind_shadow_matrix(only_missing=True)
.pipe(
lambda df: (
sns.displot(
data=df,
x="age",
hue="weight_lbs_NA",
kind="kde"
)
)
)
)
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(
riskfactors_df
.missing
.bind_shadow_matrix(only_missing=True)
.pipe(
lambda df: (
sns.boxenplot(
data=df,
x="weight_lbs_NA",
y="age",
)
)
)
)
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(
riskfactors_df
.missing
.bind_shadow_matrix(only_missing=True)
.pipe(
lambda df: (
sns.displot(
data=df,
x="age",
col="weight_lbs_NA",
facet_kws={
"sharey": False
}
)
)
)
)
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(
riskfactors_df
.missing
.bind_shadow_matrix(only_missing=True)
.pipe(
lambda df: (
sns.displot(
data=df,
x="age",
col="marital_NA",
row="weight_lbs_NA"
)
)
)
)
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def column_fill_with_dummies(
column: pd.Series,
proportion_below: float=0.10,
jitter: float=0.075,
seed: int=42
) -> pd.Series:
column = column.copy(deep=True)
# Extract values metadata.
missing_mask = column.isna()
number_missing_values = missing_mask.sum()
column_range = column.max() - column.min()
# Shift data
column_shift = column.min() - column.min() * proportion_below
# Create the "jitter" (noise) to be added around the points.
np.random.seed(seed)
column_jitter = (np.random.rand(number_missing_values) - 2) * column_range * jitter
# Save new dummy data.
column[missing_mask] = column_shift + column_jitter
return column
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plt.figure(figsize=(10, 10))
(
riskfactors_df
.select_dtypes(
exclude="category"
)
.pipe(
lambda df: df[df.columns[df.isna().any()]]
)
.missing.bind_shadow_matrix(true_string=True, false_string=False)
.apply(
lambda column: column if "_NA" in column.name else column_fill_with_dummies(column, proportion_below=0.05, jitter=0.075)
)
.assign(
nullity=lambda df: df.weight_lbs_NA | df.height_inch_NA
)
.pipe(
lambda df: (
sns.scatterplot(
data=df,
x="weight_lbs",
y="height_inch",
hue="nullity"
)
)
)
)
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missingno.heatmap(
df=riskfactors_df
)
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missingno.dendrogram(
df=riskfactors_df
)
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riskfactors_df.shape
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(
riskfactors_df
.weight_lbs
.mean()
)
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riskfactors_df.weight_lbs.size, riskfactors_df.weight_lbs.count()
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riskfactors_df.weight_lbs.mean(skipna=False)
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(
riskfactors_df
.dropna(
subset=["weight_lbs"],
how="any"
)
.shape
)
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(
riskfactors_df
.dropna(
subset=["weight_lbs", "height_inch"],
how="any"
)
.shape
)
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(
riskfactors_df
.dropna(
subset=["weight_lbs", "height_inch"],
how="all"
)
.shape
)
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(
riskfactors_df
.dropna(
subset=["weight_lbs", "height_inch"],
how="any"
)
.select_columns(["weight_lbs", "height_inch"])
.pipe(
lambda df: missingno.matrix(df)
)
)
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(
riskfactors_df
.dropna(
subset=["weight_lbs", "height_inch"],
how="all"
)
.select_columns(["weight_lbs", "height_inch"])
.pipe(
lambda df: missingno.matrix(df)
)
)
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implicit_to_explicit_df = pd.DataFrame(
data={
"name": ["lynn", np.nan, "zelda", np.nan, "shadowsong", np.nan],
"time": ["morning", "afternoon", "morning", "afternoon", "morning", "afternoon",],
"value": [350, 310, 320, 350, 310, 320]
}
)
implicit_to_explicit_df
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implicit_to_explicit_df.ffill()
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plt.figure(figsize=(10, 10))
(
riskfactors_df
.select_columns("weight_lbs", "height_inch", "bmi")
.missing.bind_shadow_matrix(true_string=True, false_string=False)
.apply(
axis="rows",
func=lambda column: column.fillna(column.mean()) if "_NA" not in column.name else column
)
.pipe(
lambda df: (
sns.displot(
data=df,
x="weight_lbs",
hue="weight_lbs_NA"
)
)
)
)
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plt.figure(figsize=(10, 10))
(
riskfactors_df
.select_columns("weight_lbs", "height_inch", "bmi")
.missing.bind_shadow_matrix(true_string=True, false_string=False)
.apply(
axis="rows",
func=lambda column: column.fillna(column.mean()) if "_NA" not in column.name else column
)
.assign(
imputed=lambda df: df.weight_lbs_NA | df.height_inch_NA
)
.pipe(
lambda df: (
sns.scatterplot(
data=df,
x="weight_lbs",
y="height_inch",
hue="imputed"
)
)
)
)
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plt.figure(figsize=(10, 10))
(
riskfactors_df
.select_columns("weight_lbs", "height_inch", "bmi")
.missing.bind_shadow_matrix(true_string=True, false_string=False)
.apply(
axis="rows",
func=lambda column: column.fillna(column.mean())
if "_NA" not in column.name
else column,
)
.pivot_longer(
index="*_NA"
)
.pivot_longer(
index=["variable", 'value'],
names_to="variable_NA",
values_to="value_NA"
)
.assign(
valid=lambda df: df.apply(axis="columns", func=lambda column: column.variable in column.variable_NA)
)
.query("valid")
.pipe(
lambda df: (
sns.displot(
data=df,
x="value",
hue="value_NA",
col="variable",
common_bins=False,
facet_kws={
"sharex": False,
"sharey": False
}
)
)
)
)
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session_info.show()
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