A package that allows you to easily profile your dataframe, check for missing values, outliers, data types.
Project description
Data Frame Profiling - A package that allows to easily profile your dataframe, check for missing values, outliers, data types.
- Import Lib
- from df_profiling import DF_Profiling
- Profile your Data:
- DF_Profiling.profiling("my_file.csv")
- Either using Google Colab or Saving it as csv file, use the filter options to easily check for:
- Data Types
- Counts
- Missing Values Count
- Missing Values Percentage
- Min Value
- Quartiles: 1st, 3rd
- Median
- Lower Bound Limits
- Upper Bound Limits
- Max Value
- Unique Values
- Spot Potential Outliers
- Save / Export your Analyses
- DF_Profiling.profiling("my_file.csv").to_csv("my_profiling.csv")
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distributions
No source distribution files available for this release.See tutorial on generating distribution archives.
Built Distribution
Filter files by name, interpreter, ABI, and platform.
If you're not sure about the file name format, learn more about wheel file names.
Copy a direct link to the current filters
File details
Details for the file easy_df_profiling-0.1.2-py3-none-any.whl.
File metadata
- Download URL: easy_df_profiling-0.1.2-py3-none-any.whl
- Upload date:
- Size: 3.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.8.0
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 |
445dea801312e9d9e5b1c5d7ffd9a3a3aaf65e3967ea9c710b84ae46ed07ede4
|
|
| MD5 |
c044fc8d8652725214b7a5f9d1a6b3e7
|
|
| BLAKE2b-256 |
e1d1377b0892149831e04c8126e32ece8a29947fd06595ae962eeb890d952897
|
