GitBook: [master] 65 pages modified

Author: risenW

README.md

@@ -11,22 +11,22 @@ D**anfo.js** is heavily inspired by the [Pandas](https://pandas.pydata.org/panda
 
 ## Main Features
 
-* Danfo.js is fast. It is built on[ Tensorflow.js](https://js.tensorflow.org/), and supports tensors out of the box. This means you can [convert Danfo.js ](api-reference/dataframe/)DataFrames to Tensors, and vice versa. 
+* Danfo.js is fast. It is built on[ Tensorflow.js](https://js.tensorflow.org/), and supports tensors out of the box. This means you can [convert danfo data structure](api-reference/dataframe/) to Tensors.
 * Easy handling of missing ****data \(represented as `NaN`\) in floating point as well as non-floating point data
 * Size mutability: columns can be inserted/deleted from DataFrame
 * Automatic and explicit alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let [`Series`](api-reference/series/), [`DataFrame`](api-reference/dataframe/), etc. automatically align the data for you in computations
-* Powerful, flexible, [groupby](api-reference/groupby/) functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
-* Make it easy to convert Arrays, JSONs, List or Objects, Tensors, and differently-indexed data structures into DataFrame objects
+* Powerful, flexible [groupby](api-reference/groupby/) functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
+* Make it easy to convert Arrays, JSONs, List or Objects, Tensors and differently-indexed data structures into DataFrame objects
 * Intelligent label-based slicing, fancy indexing, and querying of large data sets
 * Intuitive [merging](api-reference/general-functions/danfo.merge.md) and [joining](api-reference/general-functions/danfo.concat.md) data sets
-* Robust IO tools for loading data from [flat-files](api-reference/input-output/danfo.read_csv.md) \(CSV and delimited\), Excel, and JSON data format.
-* Powerful, flexible, and intiutive API for [plotting](https://app.gitbook.com/@jsdata/s/danfojs/~/drafts/-MESZnq3_VBU0EW71MxS/api-reference/plotting) DataFrames and Series interactively.
+* Robust IO tools for loading data from [flat-files](api-reference/input-output/danfo.read_csv.md) \(CSV and delimited\) and JSON data format.
+* Powerful, flexible and intutive API for [plotting](https://app.gitbook.com/@jsdata/s/danfojs/~/drafts/-MESZnq3_VBU0EW71MxS/api-reference/plotting) DataFrames and Series interactively.
 * Timeseries-specific functionality: date range generation and date and time properties.
 * Robust data preprocessing functions like [OneHotEncoders](api-reference/general-functions/danfo.onehotencoder.md), [LabelEncoders](api-reference/general-functions/danfo.labelencoder.md), and scalers like [StandardScaler](api-reference/general-functions/danfo.standardscaler.md) and [MinMaxScaler](api-reference/general-functions/danfo.minmaxscaler.md) are supported on DataFrame and Series
 
 ## Getting Started
 
-New to Danfo? Check out the getting started guides. It contains a quick introduction to D_anfo's_ main concepts and links to additional content.
+New to danfo? Check out the getting started guides. They contain an introduction to _danfo's_ main concepts and links to additional contents.
 
 {% page-ref page="getting-started.md" %}
 

api-reference/README.md

@@ -9,7 +9,7 @@ description: >-
 * [General Functions](general-functions/)
   * [Data manipulations](general-functions/#data-manipulations)
   * [Data Processing/Normalization](general-functions/#data-processing-normalization)
-  * [Top-level dealing with datetime like](general-functions/#top-level-dealing-with-datetime)
+  * [Top-level dealing with datetimelike](general-functions/#top-level-dealing-with-datetime)
 * [Input/output](input-output/)
   * [CSV](input-output/#csv)
   * [JSON](input-output/#json)

api-reference/dataframe/danfo.dataframe.addcolumn.md

@@ -21,9 +21,7 @@ danfo.DataFrame.**addColumn**\(kwargs\) \[[source](https://github.com/opensource
       <td style="text-align:left">Object</td>
       <td style="text-align:left">
         <p>{<b>column</b> : str, name of the column to add</p>
-        <p> <b>values:</b> Series<b>,</b> Array of new values to add
-          <br /> <b>inplace</b>: Default to false.</p>
-        <p>}</p>
+        <p><b> value: </b>Series<b>, </b>Array. New values to add }</p>
       </td>
       <td style="text-align:left"></td>
     </tr>
@@ -32,11 +30,13 @@ danfo.DataFrame.**addColumn**\(kwargs\) \[[source](https://github.com/opensource
 
 **Returns:**
 
+       ****return **Null**
+
 ## **Examples**
 
 ## **Add Array as a new column to DataFrame**
 
-New columns get added at the end of the DataFrame, and this happens so returns nothing,
+New columns get added at the end of the DataFrame, and this happens  so returns nothing,
 
 {% tabs %}
 {% tab title="Node" %}
@@ -53,11 +53,12 @@ let new_col = [1, 2, 3, 4]
 df.addColumn({ "column": "D", "values": new_col, inplace: true });
 
 df.print()
+
 ```
 {% endtab %}
 
 {% tab title="Browser" %}
-```text
+```
 
 ```
 {% endtab %}
@@ -111,11 +112,12 @@ let s = new dfd.Series([1, 2, 3, 4])
 df.addColumn({ "column": "D", "values": s, inplace: true });
 
 df.print()
+
 ```
 {% endtab %}
 
 {% tab title="Browser" %}
-```text
+```
 
 ```
 {% endtab %}
@@ -124,6 +126,7 @@ df.print()
 {% tabs %}
 {% tab title="Output" %}
 ```text
+
 ╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
 ║   │ A                 │ B                 │ C                 │ D                 ║
 ╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢

api-reference/dataframe/dataframe.set_index.md

@@ -37,9 +37,7 @@ danfo.DataFrame.**set\_index**\(kwargs\) \[[source](https://github.com/opensourc
 
 **Returns:**
 
-```text
-   ****return **DataFrame**
-```
+       ****return **DataFrame**
 
 ## **Examples**
 
@@ -48,6 +46,7 @@ danfo.DataFrame.**set\_index**\(kwargs\) \[[source](https://github.com/opensourc
 {% tabs %}
 {% tab title="Node" %}
 ```javascript
+
 const dfd = require("danfojs-node")
 
 let data = { "A": [-20, 30, 47.3],
@@ -60,11 +59,12 @@ df.print()
 
 df.set_index({column: "A", inplace: true})
 df.print()
+
 ```
 {% endtab %}
 
 {% tab title="Browser" %}
-```text
+```
 
 ```
 {% endtab %}
@@ -73,6 +73,7 @@ df.print()
 {% tabs %}
 {% tab title="Output" %}
 ```text
+
 ╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
 ║   │ A                 │ B                 │ C                 ║
 ╟───┼───────────────────┼───────────────────┼───────────────────╢
@@ -117,11 +118,12 @@ df.print()
 let new_index = ["a", "b", "a"]
 df.set_index({column: new_index, inplace: true})
 df.print()
+
 ```
 {% endtab %}
 
 {% tab title="Browser" %}
-```text
+```
 
 ```
 {% endtab %}
@@ -130,6 +132,7 @@ df.print()
 {% tabs %}
 {% tab title="Output" %}
 ```text
+
 ╔═══╤═══════════════════╤═══════════════════╤═══════════════════╗
 ║   │ A                 │ B                 │ C                 ║
 ╟───┼───────────────────┼───────────────────┼───────────────────╢
@@ -156,5 +159,5 @@ df.print()
 {% endtab %}
 {% endtabs %}
 
-**Note:** To reset an index to the default values, use the [DataFrame.reset\_index](dataframe.reset_index.md).
+**Note:** To reset an index to the default values, use the [DataFrame.reset\_index](dataframe.reset_index.md). 
 

api-reference/general-functions/README.md

@@ -22,7 +22,7 @@ description: Top level functions that can be called from the Danfo namespace
 
 ### Top-level dealing with datetime
 
-| [`toDateTime`](danfo.to_datetime.md) | Convert argument to datetime. |
+| [`to_datetime`](danfo.to_datetime.md) | Convert argument to datetime. |
 | :--- | :--- |
 | [`date_range`](danfo.date_range.md) | Return a fixed frequency DatetimeIndex. |
 

api-reference/general-functions/danfo.get_dummies.md

@@ -17,24 +17,18 @@ danfo.**get\_dummies**\(kwargs\) \[[source](https://github.com/opensource9ja/dan
   </thead>
   <tbody>
     <tr>
-      <td style="text-align:left">data</td>
-      <td style="text-align:left">Series or Dataframe</td>
-      <td style="text-align:left">The data to dummify</td>
-      <td style="text-align:left"></td>
-    </tr>
-    <tr>
-      <td style="text-align:left"><b>options</b>
+      <td style="text-align:left"><b>kwargs</b>
       </td>
       <td style="text-align:left">Object</td>
       <td style="text-align:left">
         <p>{</p>
-        <p><b>columns</b>: Array of column names to dummify. If not specified, all
-          categorical columns are encoded.</p>
-        <p><b>prefixSeparator</b>: String separator for created columns e.g &quot;_&quot;,</p>
+        <p><b>data</b>: Array | Series | DataFrame</p>
+        <p><b>prefix_sep</b>: String separator for created columns e.g &quot;_&quot;,</p>
         <p><b>prefix</b>: String | Array of String, of column names</p>
+        <p><b>columns</b>: [Array] columns to be encoded in DataFrame.</p>
         <p>}</p>
       </td>
-      <td style="text-align:left">{<b>prefixSeparator</b>: &quot;_&quot;}</td>
+      <td style="text-align:left">{<b>prefix_sep</b>: &quot;_&quot;}</td>
     </tr>
   </tbody>
 </table>
@@ -55,7 +49,7 @@ const dfd = require("danfojs-node")
 let datasf = ['pear', 'mango', "pawpaw", "mango", "bean"]
 let sf1 = new dfd.Series(datasf)
 
-let dum_df = dfd.get_dummies(sf1, { prefix: "fruit" })
+let dum_df = dfd.get_dummies({data: sf1, prefix:"fruit"})
 dum_df.print()
 ```
 {% endtab %}
@@ -71,19 +65,19 @@ dum_df.print()
 {% tab title="Output" %}
 ```text
 
-╔════════════╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
-║            │ fruit_pear        │ fruit_mango       │ fruit_pawpaw      │ fruit_bean        ║
-╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
-║ 0          │ 1                 │ 0                 │ 0                 │ 0                 ║
-╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
-║ 1          │ 0                 │ 1                 │ 0                 │ 0                 ║
-╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
-║ 2          │ 0                 │ 0                 │ 1                 │ 0                 ║
-╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
-║ 3          │ 0                 │ 1                 │ 0                 │ 0                 ║
-╟────────────┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
-║ 4          │ 0                 │ 0                 │ 0                 │ 1                 ║
-╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
+╔═══╤═══════════════════╤═══════════════════╤═══════════════════╤═══════════════════╗
+║   │ fruit_pear        │ fruit_mango       │ fruit_pawpaw      │ fruit_bean        ║
+╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
+║ 0 │ 1                 │ 0                 │ 0                 │ 0                 ║
+╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
+║ 1 │ 0                 │ 1                 │ 0                 │ 0                 ║
+╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
+║ 2 │ 0                 │ 0                 │ 1                 │ 0                 ║
+╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
+║ 3 │ 0                 │ 1                 │ 0                 │ 0                 ║
+╟───┼───────────────────┼───────────────────┼───────────────────┼───────────────────╢
+║ 4 │ 0                 │ 0                 │ 0                 │ 1                 ║
+╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
 ```
 {% endtab %}
 {% endtabs %}
@@ -102,7 +96,7 @@ let data = { fruits: ['pear', 'mango', "pawpaw", "mango", "bean"],
 let df = new dfd.DataFrame(data)
 df.print()
 
-let dum_df = dfd.get_dummies(df)
+let dum_df = dfd.get_dummies({ data: df })
 dum_df.print()
 ```
 {% endtab %}
@@ -165,7 +159,7 @@ let data = { fruits: ['pear', 'mango', "pawpaw", "mango", "bean"],
 let df = new dfd.DataFrame(data)
 df.print()
 
-let dum_df = dfd.get_dummies(df, { columns: ['fruits']})
+let dum_df = dfd.get_dummies({ data: df, columns: ['fruits']})
 dum_df.print()
 ```
 {% endtab %}

api-reference/general-functions/danfo.onehotencoder.md

@@ -4,11 +4,11 @@ description: Encode categorical features as a one-hot numeric array.
 
 # danfo.OneHotEncoder
 
-class danfo.**OneHotEncoder** \[[source](https://github.com/opensource9ja/danfojs/blob/e25010c26d9c423412613d820015a48ad03d5c6d/danfojs-node/src/preprocessing/encodings.js#L6)\]
+class danfo.**OneHotEncoder** \[[source](https://github.com/opensource9ja/danfojs/blob/fe56860b0a303d218d60ba71dee6abf594401556/danfojs/src/core/frame.js#L254)\]
 
 danfo.js provides the OneHotEncoder class for encoding values in Series and Arrays to one-hot numeric arrays. This is mostly used as a preprocessing step before most machine learning tasks. 
 
-The API is similar to scikit-learn's [OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html?highlight=onehotencoder#sklearn.preprocessing.OneHotEncoder), and provides a fit and transform method. 
+The API is similar to sklearn's [OneHotEncoder](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html?highlight=onehotencoder#sklearn.preprocessing.OneHotEncoder), and provides a fit and transform method. 
 
 ## **Examples**
 

api-reference/general-functions/danfo.standardscaler.md

@@ -6,7 +6,7 @@ description: Standardize features by removing the mean and scaling to unit varia
 
 class danfo.**StandScaler** \[[source](https://github.com/opensource9ja/danfojs/blob/fe56860b0a303d218d60ba71dee6abf594401556/danfojs/src/core/frame.js#L254)\]
 
-danfo.js provides the StandardScaler class for the standardization of DataFrame and Series. The standard score of a sample `x` is calculated as:
+danfo.js provides the StandardScaler class for standardization of DataFrame and Series. The standard score of a sample `x` is calculated as:
 
 > z = \(x - u\) / s