From ea8ec3989332800cbfea4927bedd7a8632cbc20f Mon Sep 17 00:00:00 2001 From: Catherine Meng Date: Thu, 30 Jan 2025 15:22:45 -0800 Subject: [PATCH] fix(doc): fix package name has been used --- docs/example.ipynb | 4 ++-- src/pyeda31/check_csv.py | 2 +- src/pyeda31/data_summary.py | 2 +- src/pyeda31/pymissing_values_summary.py | 2 +- 4 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/example.ipynb b/docs/example.ipynb index 6b7d1c9..d655ce6 100644 --- a/docs/example.ipynb +++ b/docs/example.ipynb @@ -6,7 +6,7 @@ "source": [ "# Example usage\n", "\n", - "Here we will demonstrate how to effectively use the `pyeda` package to validate the format of data files and conduct basic exploratory data analysis. The steps include checking data file format, identifying missing values, and generating a summary of the data.\n", + "Here we will demonstrate how to effectively use the `pyeda31` package to validate the format of data files and conduct basic exploratory data analysis. The steps include checking data file format, identifying missing values, and generating a summary of the data.\n", "\n", "## Imports" ] @@ -344,7 +344,7 @@ "source": [ "## Conclusion\n", "\n", - "The `pyeda` package offers a user-friendly and efficient solution for validating data files and performing essential exploratory data analysis tasks. From checking file formats and identifying missing values to generating data statistics summaries, this tool simplifies the preprocessing stage, allowing you to focus on extracting deeper insights and making informed decisions. Try it out with your own dataset and experience its ease of use!" + "The `pyeda31` package offers a user-friendly and efficient solution for validating data files and performing essential exploratory data analysis tasks. From checking file formats and identifying missing values to generating data statistics summaries, this tool simplifies the preprocessing stage, allowing you to focus on extracting deeper insights and making informed decisions. Try it out with your own dataset and experience its ease of use!" ] } ], diff --git a/src/pyeda31/check_csv.py b/src/pyeda31/check_csv.py index 339d681..296ad1a 100644 --- a/src/pyeda31/check_csv.py +++ b/src/pyeda31/check_csv.py @@ -15,7 +15,7 @@ def check_csv(file_path): Examples -------- - >>> from pyeda.check_csv import check_csv + >>> from pyeda31.check_csv import check_csv >>> check_csv("../data/raw/data.csv") """ # Check if file extension is .csv diff --git a/src/pyeda31/data_summary.py b/src/pyeda31/data_summary.py index d8fefc1..91696cb 100644 --- a/src/pyeda31/data_summary.py +++ b/src/pyeda31/data_summary.py @@ -22,7 +22,7 @@ def get_summary_statistics(df, col = None): Examples -------- - >>> from pyeda.data_summary import get_summary_statistics + >>> from pyeda31.data_summary import get_summary_statistics >>> get_summary_statistics(df) """ if col is None: diff --git a/src/pyeda31/pymissing_values_summary.py b/src/pyeda31/pymissing_values_summary.py index ded5659..e754166 100644 --- a/src/pyeda31/pymissing_values_summary.py +++ b/src/pyeda31/pymissing_values_summary.py @@ -15,7 +15,7 @@ def missing_values_summary(df): Examples -------- - >>> from pyeda.pymissing_value import missing_values_summary + >>> from pyeda31.pymissing_value import missing_values_summary >>> missing_values_summary(df) """ # Calculate the count of missing values for each column