Data Stats Library

This library contains a collection of functions designed to make working with pandas data sets easier. Below, you will find a description of each of them:

Installation

Open a terminal and run:

pip install git+https://github.com/dmenares93/stata_py

Functions

1. tab

Function that replicates the tabulation function, providing a table with counts and percentages.

tab(df: pd.DataFrame, col: Union[str, List[str]], nofreq: bool = False,
sort: bool = False, round_decimals: int = 2, reset_index: bool = True,
missing: bool = False, total: bool = False, if_stata: str = None,
percent: str = None, w: pd.Series = None) -> pd.DataFrame

Parameters:

• df: pd.DataFrame - The DataFrame containing the data to be tabulated.
• col: Union[str, List[str]] - Column(s) from df to tabulate. Either a single string or a list of one or two strings.
• nofreq (optional): bool - If False, frequencies are computed; if True, frequencies are not computed. Default is False.
• sort (optional): bool - If True, the results are sorted by frequencies. Default is False.
• round_decimals (optional): int - Number of decimals to which the results are rounded. Default is 2.
• reset_index (optional): bool - If True, deletes the index of variables. Default is True.
• missing (optional): bool - If True, handles missing values. Default is False.
• total (optional): bool - If True, adds the total. Default is False.
• if_stata (optional): str - Control conditions, following the syntax used in Stata. Default is None.
• percent (optional): str - Control mode percentage with the options: "col", "row", or "cell". Default is None.
• w (optional): pd.Series - Expansion factor, typically used for weighted survey data. Default is None.

Returns:

• pd.DataFrame - Table with the tabulation results. Contains counts and percentages based on the given parameters.

Raises:

• TypeError - If df is not a DataFrame or col is not a string or a list of one or two strings.

Notes:

• If w is provided, each value in the specified col is multiplied by the corresponding value in w.
• The if_stata parameter allows for complex conditions to filter the DataFrame before tabulation.
• The percent parameter controls how percentages are computed: by column ("col"), by row ("row"), or by cell ("cell").

Examples:

from stata_py.stats import tab
from gapminder import gapminder as df

tab(df,"continent")
tab(df,"country", if_stata = "pop>100000000", nofreq = True)
tab(df,"country", if_stata = "pop>100000000", total = True, round_decimals = 3)
tab(df,["country","continent"], if_stata = "pop>100000000")

2. table

Generates a table that computes various statistics based on the given variables.

def table(df: pd.DataFrame, 
          var: Union[str, List[str]], 
          stats: str,
          pivot:bool = True,
          round_decimals: int = 2,
          if_stata: str = None,
          w: pd.Series = None) -> pd.DataFrame:

Parameters:

• df: pd.DataFrame - Dataframe on which to evaluate the function.
• var: Union[str, List[str]] - Variables from df to tabulate. Can be a string or a list of one or two strings.
• stats: str - Statistics to be calculated, must match the available operations. The accepted operations include:
  • sum - Sum of elements
  • mean - Compute the arithmetic mean
  • median - Compute the median
  • min - Find the minimum value
  • max - Find the maximum value
  • prod - Compute the product of elements
  • std - Compute the standard deviation
  • var - Compute the variance
  • count - Count the number of non-null elements
  • nunique - Count the number of unique elements
  • first - Get the first non-null element
  • last - Get the last non-null element
  • p1/p100 - Compute the percentile
• pivot (optional): bool - If True, transposes the table results (useful for cross-tabulations). Default is True.
• round_decimals (optional): int - Number of decimal places to round the results to. Default is 2.
• if_stata (optional): str - Control conditions, following the syntax used in Stata. Default is None.
• w (optional): pd.Series - Expansion factor, typically used in survey-weighted data. Default is None.

Returns:

• pd.DataFrame - Table with the tabulation results, including the requested statistics.

Raises:

• TypeError - If df is not a DataFrame or var is not a string or a list of one or two strings.

Notes:

• Available statistics include: sum, mean, median, minimum, maximum, product, standard deviation, variance, count, number of unique elements, first and last non-null element, percentiles.
• The if_stata parameter allows complex conditions to filter the DataFrame before tabulation.
• If w is provided, it is used to weight the calculated statistics.

Examples:

from stata_py.stats import table
from gapminder import gapminder as df

table(df,"continent", if_stata = "pop>100000000", stats= "mean gdpPercap", round_decimals = 3)
table(df,"continent", if_stata = "pop>100000000", stats= "mean gdpPercap lifeExp median lifeExp")
table(df,["continent","year"], if_stata = "year>2000", stats= "mean gdpPercap max lifeExp")
table(df,["continent","year"], if_stata = "year>2000", stats= "mean gdpPercap", pivot = False)

3. count

Returns the count of rows in the DataFrame, optionally filtered by a condition.

def count(df: pd.DataFrame, 
          if_stata: str = None) -> int:

Parameters:

• df: pd.DataFrame - The DataFrame for which to count the rows.
• if_stata (optional): str - Control conditions to filter the DataFrame before counting rows, following the syntax used in Stata. Default is None.

Returns:

• int - Count of rows in the DataFrame after applying any specified conditions.

Notes:

• The if_stata parameter allows for complex conditions to filter the DataFrame before counting rows.
• If no conditions are specified, the function returns the total number of rows in the DataFrame.

Examples:

from stata_py.stats import table
from gapminder import gapminder as df

count(df)
ount(df,if_stata = "lifeExp>70")

4. to_excel

Function to export table in excel

def to_excel(data: Union[pd.DataFrame, 
                  List[pd.DataFrame]], path: str, 
                  sheet_names: List[str] = None) -> None:

Parameters:

• data: Union[pd.DataFrame, List[pd.DataFrame]] - DataFrame or a list of DataFrames that will be written to the Excel file.
• path: str - Full path to the Excel file where the data will be written.
• sheet_names (optional): List[str] - List of names for the sheets within the Excel file. If omitted, sheets will be named as 'Sheet1', 'Sheet2', etc.

Returns:

• None - The function does not return any value but saves the Excel file to the specified location.

Notes:

• The function applies custom styling to the cells, including thin borders, a maximum column width of 25, and blue fill with white font in the first row and first column. The function uses the 'openpyxl' engine for writing the Excel file.

Examples:

from stata_py.stats import table
from gapminder import gapminder as df

df1 = table(df,["continent","year"], if_stata = "year>2000", stats= "mean gdpPercap max lifeExp")
df2 = table(df,["continent","year"], if_stata = "year<=2000", stats= "mean gdpPercap max lifeExp")

data = [d1,d2]
sheet_names = [">200","<=2000"]
path = "/stats.xlsx"
 to_excel(data,path, sheet_names = sheet_names )

License

MIT License