rgwml 0.0.346

Manipulate data with code that is less a golden retriever, and more a Samurai's sword

RGWML

By Ryan Gerard Wilson (https://ryangerardwilson.com)

Manipulate data with code that is less a golden retriever, and more a Samurai's sword

1. Install

---

sudo apt update
sudo apt install ffmpeg
pip3 install --upgrade rgwml

2. Import & Load Data

---

import rgwml as r

# For 99% use cases a Pandas df is good enough
d1 = r.p()
d1.fp('/path/to/your/file')

# For the remaining 1%
d2 = r.d()
d2.fp('/path/to/your/file')

3. Create a .rgwfuncsrc file

---

A .rgwfuncsrc file (located at `vi ~/.rgwfuncsrc) is required for MSSQL, CLICKHOUSE, MYSQL, GOOGLE BIG QUERY, SLACK, TELEGRAM, and GMAIL integrations.

{
  "db_presets" : [
    {
      "name": "mssql_db9",
      "db_type": "mssql",
      "host": "",
      "username": "",
      "password": "",
      "database": ""
    },
    {
      "name": "clickhouse_db7",
      "db_type": "clickhouse",
      "host": "",
      "username": "",
      "password": "",
      "database": ""
    },
    {
      "name": "mysql_db2",
      "db_type": "mysql",
      "host": "",
      "username": "",
      "password": "",
      "database": ""
    },
    {
      "name": "bq_db1",
      "db_type": "google_big_query",
      "json_file_path": "",
      "project_id": ""
    }
  ],
  "vm_presets": [
    {
      "name": "main_server",
      "host": "",
      "ssh_user": "",
      "ssh_key_path": ""
    }
  ],
  "cloud_storage_presets": [
    {
      "name": "gcs_bucket_name",
      "credential_path": "/path/to/your/credentials.json"
    }
  ],
  "telegram_bot_presets": [
    {
      "name": "rgwml-bot",
      "chat_id": "",
      "bot_token": ""
    }
  ],
  "slack_bot_presets": [
    {
      "name": "labs-channel",
      "channel_id": "",
      "bot_token": ""
    }
  ],
  "gmail_bot_presets": [
    {
      "name": "[e-mail]",
      "service_account_credentials_path": "/path/to/your/credentials.json"
    }
  ]
}

4. r.p() Class Methods

---

Instantiate this class by d = r.p()

4.1. LOAD

# From raw data
d.frd(['col1','col2'],[[1,2,3],[4,5,6]])

# From path
d.fp('/absolute/path')

# From Directory (select from your last 7 recently modified files in your Desktop/Downloads/Documents directories)
d.fd()

# From query
d.fq('rgwfucsrc_db_preset_name','SELECT * FROM your_table')

# FROM chunkable query
d.fcq('rgwfuncsrc_db_preset_name', 'SELECT * FROM your_table', chunk_size)

4.2. INSPECT

# Describe
d.d()

# Print
d.pr()

# First n rows
d.fnr('n')

# Last n rows
d.lnr('n')

# Top n unique values for specified columns
d.tnuv(n, ['col1', 'col2'])

# Bottom n unique values for specified columns
d.bnuv(n, ['col1', 'col2'])

# Is empty. Returns boolean, not chainable.
d.ie()

# Memory usage print.
d.mem()

# Print correlation
d.prc([('column1','column2'), ('column3','column4')])

# Print n frequency linear. Optional: order_by (str), which has options: ASC, DESC, FREQ_ASC, FREQ_DESC (default)
d.pnfl(5,'Column1,Columns')

# Print n frequency cascading. Optional: order_by (str), which has options: ASC, DESC, FREQ_ASC, FREQ_DESC (default)
d.pnfc(5,'Column1,Columns')

4.3. APPEND

# Append boolean classification column
d.abc('column1 > 30 and column2 < 50', 'new_column_name')

# Append DBSCAN cluster column. Optional: visualize (boolean)
d.adbscancc('Column1,Column2', 'new_cluster_column_name', eps=0.5, min_samples=5, visualize=True)

# Append n-cluster column. Available operations: KMEANS/ AGGLOMERATIVE/ MEAN_SHIFT/ GMM/ SPECTRAL/ BIRCH. Optional: visualize (boolean), n_cluster_finding_method (str) i.e. ELBOW/ SILHOUETTE/ FIXED:n (specify a number of n clusters).
d.ancc('Column1,Column2', 'KMEANS', 'new_cluster_column_name', n_clusters_finding_method='FIXED:5', visualize=True)

# Append percentile classification column
d.apc('0,25,50,75,100', 'column_to_be_analyzed', 'new_column_name')

# Append ranged classification column
d.arc('0,500,1000,2000,5000,10000,100000,1000000', 'column_to_be_analyzed', 'new_column_name')

# Append ranged date classification column
d.ardc('2024-01-01,2024-02-01,2024-03-01', 'date_column', 'new_date_classification')    

# Append count of timestamps after reference time. Requires values in YYYY-MM-DD or YYYY-MM-DD HH:MM:SS format.
d.atcar('comma_separated_timestamps_column', 'reference_date_or_timestamps_column', 'new_column_count_after_reference')

# Append count of timestamps before reference time. Requires values in YYYY-MM-DD or YYYY-MM-DD HH:MM:SS format.
d.atcbr('comma_separated_timestamps_column', 'reference_date_or_timestamp_column', 'new_column_count_before_reference')

4.4. DOCUMENTATION

# Prints docs. Optional parameter: method_type_filter (str) egs. 'APPEND, PLOT'
d.doc()

4.5. JOINS

# Union join
d.uj(d2)

# Bag union join
d.buj(d2)

# Left join
d.lj(d2,'table_a_id','table_b_id')

# Right join
d.rj(d2,'table_a_id','table_b_id')

4.6. PERSIST

# Save (saves as csv (default) or h5, to desktop (default) or path)
d.s('/filename/or/path')
d.s() #If the dataframe was loaded from a source with an absolute path, calling the s method without an argument will save at the same path

4.7. PLOT

# Plot correlation heatmap for the specified columns. Optional param: image_save_path (str)
d.pcr(y='Column1, Column2, Column3')

# Plot distribution histograms for the specified columns. Optional param: image_save_path (str)
d.pdist(y='Column1, Column2, Column3')

# Plot line chart. Optional param: x (str), i.e. a single column name for the x axis eg. 'Column5', image_save_path (str)
d.plc(y='Column1, Column2, Column3')

# Plot Q-Q plots for the specified columns. Optional param: image_save_path (str)
d.pqq(y='Column1, Column2, Column3')

4.8. PREDICT

# Append XGB training labels based on a ratio string. Specify a ratio a:b:c to split into TRAIN, VALIDATE and TEST, or a:b to split into TRAIN and TEST.
d.axl('70:20:10')

# Append XGB regression predictions. Assumes labelling by the .axl() method. Optional params: boosting_rounds (int), model_path (str)
d.axlinr('target_column','feature1, feature2, feature3','prediction_column_name')

# Append XGB logistic regression predictions. Assumes labeling by the .axl() method. Optional params: boosting_rounds (int), model_path (str)
d.axlogr('target_column','feature1, feature2, feature3','prediction_column_name')

4.9. TINKER

# Cascade sort by specified columns.
d.cs(['Column1', 'Column2'])

# Filter
d.f("col1 > 100 and Col1 == Col3 and Col5 == 'XYZ'")

# Filter Indian Mobiles
d.fim('mobile')

# Filter Indian Mobiles (complement)
d.fimc('mobile')

# Make numerically parseable by defaulting to zero for specified column
d.mnpdz(['Column1', Column2])

# Rename columns
d.rnc({'old_col1': 'new_col1', 'old_col2': 'new_col2'})

4.10. TRANSFORM

# Group. Permits multiple aggregations on the same column. Available agg options: sum, mean, min, max, count, size, std, var, median, css (comma-separated strings), etc.
d.(['group_by_columns'], ['column1::sum', 'column1::count', 'column3::sum'])

# Pivot. Optional param: seg_columns. Available agg options: sum, mean, min, max, count, size, std, var, median, etc.
d.p(['group_by_cols'], 'values_to_agg_col', 'sum', ['seg_columns'])

5. r.d() Methods

---

Instantiate this class by d = r.d()

5.1. LOAD

# From raw data
d.frd(['col1','col2'],[[1,2,3],[4,5,6]])

# From path
d.fp('/absolute/path')

5.2. INSPECT

# Print
d.pr()

5.3. DOCUMENTATION

# Prints docs. Optional parameter: method_type_filter (str) egs. 'APPEND, PLOT'
d.doc()

5.4. JOINS

# Union join
d.uj(d2)

5.5. PERSIST

# Save (saves as csv (default) or h5, to desktop (default) or path)
d.s('/filename/or/path')

5.6. TINKER

# Filter Indian Mobiles
d.fim('mobile')

# Filter Indian Mobiles (complement)
d.fimc('mobile')

5.7. TRANSFORM

# Group. Permits multiple aggregations on the same column. Available agg options: sum, mean, min, max, count, size, std, var, median, css (comma-separated strings), etc.
d.(['group_by_columns'], ['column1::sum', 'column1::count', 'column3::sum'])

# Pivot. Optional param: seg_columns. Available agg options: sum, mean, min, max, count, size, std, var, median, etc.
d.p(['group_by_cols'], 'values_to_agg_col', 'sum', ['seg_columns'])