Module milanesas.eda_helper
Functions
explode_pie(pie_size): Generates a list of values to explode slices of a pie chart.
Creates a list of random values between 0.01 and 0.05, suitable for
visually exploding slices of a pie chart. The number of values in the
list is determined by the pie_size argument.
Args:
pie_size: An integer representing the number of slices in the pie chart.
Returns:
A list of floating-point values between 0.01 and 0.05, with a length
equal to `pie_size`.
Example:
>>> import pandas as pd
>>> imp_df = pd.DataFrame({'A': [10, 20, 30]})
>>> explode_values = explode_pie(imp_df.size)
>>> print(explode_values) # Example output: [0.03546542, 0.01237543, 0.04892357]
get_column_uniques(df, col): Prints unique values in a DataFrame column, handling semicolon-separated lists.
Prints the unique values found within a specified column of a DataFrame. Treats semicolon-separated values within cells as individual elements.
Args:
df (pandas.DataFrame): The DataFrame to analyze.
col (str): The name of the column to extract unique values from.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'exp_en_IT': ['A;B;C', 'A;B', 'D']})
>>> print_column_uniques(df, "exp_en_IT")
{'A', 'B', 'C', 'D'}
get_normal_uniques_col_count(df, col): Counts occurrences of unique values (including those within semicolon-separated lists), normalizing counts by row count.
Calculates the count of each unique value within a specified column of a DataFrame, handling cases where cells contain multiple values separated by semicolons. Normalizes the counts by dividing them by the total number of rows in the DataFrame.
Args:
df (pandas.DataFrame): The input DataFrame.
col (str): The name of the column to analyze.
Returns:
dict: A dictionary where keys represent unique values from the column and values
represent their normalized counts (fraction of total rows).
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'educacion': ['A;B', 'A', 'A;C', 'B']})
>>> normalized_counts = get_normal_uniques_col_count(df, "educacion")
>>> print(normalized_counts)
{'A': 0.75, 'B': 0.5, 'C': 0.25}
get_percentage(value): Formats a value as a percentage string.
Converts a numerical value into a percentage representation, rounded to the nearest integer, and returns it as a formatted string with a percentage sign.
Args:
value (float): The numerical value to convert to a percentage.
Returns:
str: The formatted percentage string (e.g., "42%").
Example:
>>> percentage_string = get_percentage(0.4235)
>>> print(percentage_string) # Output: "42%"
get_uniques_col_count(df, col): Counts occurrences of unique values (including those within semicolon-separated lists).
Calculates the count of each unique value within a specified column of a DataFrame, handling cases where cells contain multiple values separated by semicolons.
Args:
df (pandas.DataFrame): The input DataFrame.
col (str): The name of the column to analyze.
Returns:
dict: A dictionary where keys represent unique values from the column and values
represent their counts.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'educacion': ['A;B', 'A', 'A;C', 'B']})
>>> counts = get_uniques_col_count(df, "educacion")
>>> print(counts)
{'A': 3, 'B': 2, 'C': 1}
make_custom_horizontal_bar(df, col, titulo, x_label, y_label, legend): Creates a horizontal bar chart from a pre-formatted DataFrame.
Generates a horizontal bar chart from a DataFrame that's already been prepared with specific column names ("Category" for categories and "count" for values).
Args:
df (pandas.DataFrame): The input DataFrame, containing a 'Category' column
and a 'count' column.
col (str): Unused in this function, but kept for consistency with other
charting functions.
titulo (str): The title of the chart.
x_label (str): The label for the x-axis.
y_label (str): The label for the y-axis.
legend (bool): True to display a legend, False to hide it.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'Category': ['A', 'B', 'A', 'C'], 'count': [4, 2, 3, 1]})
>>> make_custom_horizontal_bar(df, "col", "Carreras o especialidades", "Total", "Carreras / Especialidades", False)
make_dataframe(df, col, cat_col, count_col):
make_df(df, col, x_label, y_label): Creates a DataFrame counting occurrences of unique values (including those within semicolon-separated lists).
Constructs a new DataFrame that tallies the number of occurrences of each unique value within a specified column of a given DataFrame. Handles cases where cells contain multiple values separated by semicolons.
Args:
df (pandas.DataFrame): The input DataFrame.
col (str): The name of the column to analyze.
x_label (str): The label for the column containing unique values in the output DataFrame.
y_label (str): The label for the column containing counts in the output DataFrame.
Returns:
pandas.DataFrame: A new DataFrame with two columns:
- x_label: Contains the unique values from the specified column.
- y_label: Contains the counts of those values.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'educacion': ['A;B', 'A', 'C;B', 'D']})
>>> new_df = make_df(df, "educacion", "categories", "count")
>>> print(new_df)
categories count
0 A 2
1 B 2
2 C 1
3 D 1
make_horizontal_bar(df, col, titulo, x_label, y_label, legend): Creates a horizontal bar chart for a specified column in a DataFrame.
Generates a horizontal bar chart that visualizes the counts of unique values within a given column of a DataFrame.
Args:
df (pandas.DataFrame): The input DataFrame.
col (str): The name of the column to visualize.
titulo (str): The title of the chart.
x_label (str): The label for the x-axis.
y_label (str): The label for the y-axis.
legend (bool): True to display a legend, False to hide it.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'carr_especialidades': ['A', 'B', 'A', 'C', 'B']})
>>> make_horizontal_bar(df, "carr_especialidades", "Carreras o especialidades", "Total", "Carreras / Especialidades", False)
make_horizontal_grouped_chart(df, g1, g2, col, labels, config): Creates a horizontal grouped bar chart comparing values between two groups.
Generates a horizontal bar chart with two sets of bars, one for each group (g1 and g2), comparing their counts for unique values in a specified column. Labels, title, and other chart elements are customized using a configuration dictionary.
Args:
df (pandas.DataFrame): The DataFrame containing the data.
g1 (pandas.DataFrame): A subset of the DataFrame representing the first group.
g2 (pandas.DataFrame): A subset of the DataFrame representing the second group.
col (str): The name of the column to compare values for.
labels (list): A list of unique values from the column to use as labels.
config (dict): A configuration dictionary with keys:
- title (str): The title of the chart.
- c1_label (str): The label for the first group's bars.
- c2_label (str): The label for the second group's bars.
- xlabel (str): The label for the x-axis.
- ylabel (str): The label for the y-axis.
Raises:
ValueError: If the specified column does not exist in the DataFrame.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'exp_en_IT': ['A', 'B', 'A', 'C', 'B'], 'gender': ['MAN', 'WOMAN', 'MAN', 'MAN', 'WOMAN']})
>>> gen = df.groupby('gender')
>>> group_config = {
... 'title': "exp_en_IT by Gender",
... 'c1_label': "MAN",
... 'c2_label': "WOMAN",
... 'xlabel': "Count",
... 'ylabel': "exp_en_IT level"
... }
>>> make_horizontal_grouped_chart(df, gen.get_group("MAN"), gen.get_group("WOMAN"), "exp_en_IT", df["exp_en_IT"].unique(), group_config)
make_normalized_df(df, col): Creates a DataFrame with normalized counts of unique values, handling semicolon-separated lists.
Constructs a new DataFrame that displays the percentage of occurrences for each unique value within a specified column of a given DataFrame. Values in cells can be separated by semicolons, and each unique value within a semicolon-separated list is counted separately.
Args:
df (pandas.DataFrame): The input DataFrame.
col (str): The name of the column to analyze.
Returns:
pandas.DataFrame: A new DataFrame with two columns:
- categories: Contains the unique values from the specified column.
- total count: Contains the percentage of occurrences for each unique value.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'imp_ed_formal': ['A;B', 'A', 'C;B', 'A;B']})
>>> normalized_counts = make_normalized_df(df, "imp_ed_formal")
>>> print(normalized_counts)
total count
categories
A 50.0
B 50.0
C 25.0
make_vertical_grouped_chart(df, g1, g2, col, labels, config): Creates a vertical grouped bar chart comparing values between two groups.
Generates a vertical bar chart with two sets of bars, one for each group (g1 and g2), comparing their counts for unique values in a specified column. Labels, title, and other chart elements are customized using a configuration dictionary.
Args:
df (pandas.DataFrame): The DataFrame containing the data.
g1 (pandas.DataFrame): A subset of the DataFrame representing the first group.
g2 (pandas.DataFrame): A subset of the DataFrame representing the second group.
col (str): The name of the column to compare values for.
labels (list): A list of unique values from the column to use as labels.
config (dict): A configuration dictionary with keys:
- title (str): The title of the chart.
- c1_label (str): The label for the first group's bars.
- c2_label (str): The label for the second group's bars.
- xlabel (str): The label for the x-axis.
- ylabel (str): The label for the y-axis.
Raises:
ValueError: If the specified column does not exist in the DataFrame.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'edad_actual': [25, 30, 30, 25, 35], 'gender': ['MAN', 'WOMAN', 'MAN', 'MAN', 'WOMAN']})
>>> gen = df.groupby('gender')
>>> group_config = {
... 'title': "edad_actual by Gender",
... 'c1_label': "Hombres",
... 'c2_label': "Mujeres",
... 'xlabel': "edad_actual level",
... 'ylabel': "Count"
... }
>>> make_vertical_grouped_chart(df, gen.get_group("MAN"), gen.get_group("WOMAN"), "edad_actual", df["edad_actual"].unique(), group_config)
percentage_to_normal(val): Formats a Series of percentage values with rounding and percentage sign.
Converts a Series of values to percentages, rounds them to one decimal place, and adds a percentage sign. The output is formatted as a string.
Args:
val (pandas.Series): A Series containing numerical values.
Returns:
pandas.Series: A Series with the same index as the input, but containing
formatted percentage strings.
Example:
>>> import pandas as pd
>>> s = pd.Series([0.1234, 0.5678, 0.9012])
>>> formatted_percentages = percentage_to_normal(s)
>>> print(formatted_percentages)
0 12.3 %
1 56.8 %
2 90.1 %
dtype: object
print_column_uniques(df, col):
random_hex():
replace_column_content(df, col, repl): Replaces values in a DataFrame column using a replacement dictionary.
Modifies a DataFrame column in-place by replacing values based on a provided dictionary. The replacement dictionary maps original values to their desired replacements. Regular expressions can be used for flexible matching.
Args:
df (pandas.DataFrame): The DataFrame to modify.
col (str): The name of the column to modify.
repl (dict): A dictionary containing replacement mappings, where keys
represent original values and values represent their replacements.
Raises:
ValueError: If the specified column does not exist in the DataFrame.
Example:
>>> import pandas as pd
>>> df = pd.DataFrame({'genero': ['HOMBRE', 'MUJER', 'NO COMPARTO']})
>>> gen_repl = {
... "HOMBRE": "MAN",
... "MUJER": "WOMAN",
... "NO COMPARTO": "DONT SHARE",
... }
>>> replace_column_content(df, "genero", gen_repl)
>>> print(df) # Output:
genero
0 MAN
1 WOMAN
2 DONT SHARE