#!/usr/bin/env python
"""Pandas utility functions."""
# Core Library
import datetime as dt
import logging
from typing import Any, Dict, List, Tuple
# Third party
import pandas as pd
import pkg_resources
# First party
import mpu.shell
countries_file = pkg_resources.resource_filename("mpu", "data/countries.csv")
countries = pd.read_csv(countries_file)
logger = logging.getLogger("mpu")
[docs]def example_df():
"""Create an example dataframe."""
country_names = ["Germany", "France", "Indonesia", "Ireland", "Spain", "Vatican"]
population = [82521653, 66991000, 255461700, 4761865, 46549045, None]
population_time = [
dt.datetime(2016, 12, 1),
dt.datetime(2017, 1, 1),
dt.datetime(2017, 1, 1),
None, # Ireland
dt.datetime(2017, 6, 1), # Spain
None,
]
euro = [True, True, False, True, True, True]
df = pd.DataFrame(
{
"country": country_names,
"population": population,
"population_time": population_time,
"EUR": euro,
}
)
df = df[["country", "population", "population_time", "EUR"]]
return df
[docs]def describe(df, dtype=None):
"""
Print a description of a Pandas dataframe.
Parameters
----------
df : Pandas.DataFrame
dtype : dict
Maps column names to types
"""
if dtype is None:
dtype = {}
print("Number of datapoints: {datapoints}".format(datapoints=len(df)))
column_info, column_info_meta = _get_column_info(df, dtype)
if len(column_info["int"]) > 0:
_describe_int(df, column_info)
if len(column_info["float"]) > 0:
_describe_float(df, column_info)
if len(column_info["category"]) > 0:
_describe_category(df, column_info, column_info_meta)
if len(column_info["time"]) > 0:
_describe_time(df, column_info, column_info_meta)
if len(column_info["other"]) > 0:
_describe_other(df, column_info, column_info_meta)
column_types = {}
for column_type, columns in column_info.items():
for column_name in columns:
if column_type == "other":
column_type = "str"
column_types[column_name] = column_type
return column_types
def _get_column_info(df: pd.DataFrame, dtype: Dict[str, str]) -> Tuple[Dict, Dict]:
column_info: Dict[str, List[str]] = {
"int": [],
"float": [],
"category": [],
"other": [],
"time": [],
}
float_types = ["float64"]
integer_types = ["int64", "uint8"]
time_types = ["datetime64[ns]"]
other_types = ["object", "category"]
column_info_meta: Dict[str, Dict[str, Any]] = {}
for column_name in df:
column_info_meta[column_name] = {}
counter_obj = df[column_name].value_counts()
value_list = counter_obj.keys().tolist()
value_count = len(value_list)
is_suspicious_cat = (
value_count <= 50
and str(df[column_name].dtype) != "category"
and column_name not in dtype
)
if is_suspicious_cat:
logger.warning(
"Column '{}' has only {} different values ({}). "
"You might want to make it a 'category'".format(
column_name, value_count, value_list
)
)
if len(value_list) > 0:
top_count_val = counter_obj.tolist()[0]
else:
top_count_val = None
column_info_meta[column_name]["top_count_val"] = top_count_val
column_info_meta[column_name]["value_list"] = value_list
column_info_meta[column_name]["value_count"] = value_count
is_int_type = (
df[column_name].dtype in integer_types
or column_name in dtype
and dtype[column_name] in integer_types
)
is_float_type = (
df[column_name].dtype in float_types
or column_name in dtype
and dtype[column_name] in float_types
)
is_cat_type = (
str(df[column_name].dtype) in ["category", "bool"]
or column_name in dtype
and dtype[column_name] in ["category", "bool"]
)
is_time_type = str(df[column_name].dtype) in time_types
is_other_type = (
str(df[column_name].dtype) in other_types
or column_name in dtype
and dtype[column_name] in other_types
)
if is_int_type:
column_info["int"].append(column_name)
elif is_float_type:
column_info["float"].append(column_name)
elif is_cat_type:
column_info["category"].append(column_name)
elif is_other_type:
column_info["other"].append(column_name)
elif is_time_type:
column_info["time"].append(column_name)
else:
logger.warning(
"mpu.pd.describe does not know type '{}'".format(df[column_name].dtype)
)
return column_info, column_info_meta
def _describe_int(df, column_info):
print("\n## Integer Columns")
table = [
["Column name", "Non-nan", "mean", "std", "min", "25%", "50%", "75%", "max"]
]
for column_name in column_info["int"]:
row = []
row.append(column_name)
row.append(sum(df[column_name].notnull()))
row.append(df[column_name].mean())
row.append(df[column_name].std())
row.append(df[column_name].min())
row.append(df[column_name].quantile(0.25))
row.append(df[column_name].quantile(0.50))
row.append(df[column_name].quantile(0.75))
row.append(max(df[column_name]))
table.append(row)
mpu.shell.print_table(table)
def _describe_float(df, column_info):
print("\n## Float Columns")
table = [
["Column name", "Non-nan", "mean", "std", "min", "25%", "50%", "75%", "max"]
]
for column_name in column_info["float"]:
row = []
row.append(column_name)
row.append(sum(df[column_name].notnull()))
row.append("{:0.2f}".format(df[column_name].mean()))
row.append("{:0.2f}".format(df[column_name].std()))
row.append("{:0.2f}".format(df[column_name].min()))
row.append("{:0.2f}".format(df[column_name].quantile(0.25)))
row.append("{:0.2f}".format(df[column_name].quantile(0.50)))
row.append("{:0.2f}".format(df[column_name].quantile(0.75)))
row.append("{:0.2f}".format(max(df[column_name])))
table.append(row)
mpu.shell.print_table(table)
def _describe_category(df, column_info, column_info_meta):
print("\n## Category Columns")
table = [["Column name", "Non-nan", "unique", "top el", "top (count)", "rest"]]
for column_name in column_info["category"]:
row = []
row.append(column_name)
row.append(sum(df[column_name].notnull()))
row.append(len(df[column_name].unique()))
row.append(column_info_meta[column_name]["value_list"][0])
row.append(column_info_meta[column_name]["top_count_val"])
rest = str(column_info_meta[column_name]["value_list"][1:])[:40]
row.append(rest)
table.append(row)
mpu.shell.print_table(table)
def _describe_time(df, column_info, column_info_meta):
print("\n## Time Columns")
table = [
["Column name", "Non-nan", "unique", "top el", "top (count)", "min", "max"]
]
for column_name in column_info["time"]:
row = []
row.append(column_name)
row.append(sum(df[column_name].notnull()))
row.append(len(df[column_name].unique()))
row.append(column_info_meta[column_name]["value_list"][0])
row.append(column_info_meta[column_name]["top_count_val"])
row.append(df[column_name].min())
row.append(df[column_name].max())
table.append(row)
mpu.shell.print_table(table)
def _describe_other(df, column_info, column_info_meta):
print("\n## Other Columns")
table = [["Column name", "Non-nan", "unique", "top", "(count)", "rest"]]
for column_name in column_info["other"]:
row = []
row.append(column_name)
row.append(sum(df[column_name].notnull()))
row.append(len(df[column_name].unique()))
row.append(column_info_meta[column_name]["value_list"][0])
row.append(column_info_meta[column_name]["top_count_val"])
rest = str(column_info_meta[column_name]["value_list"][1:])[:40]
row.append(rest)
table.append(row)
mpu.shell.print_table(table)