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Apr 2, 2025
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Fix camel case for csv json cum #25

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477040d
convert *_csv to *CSV, *_json to *JSON
May 4, 2023
0a4dd1c
change group.cum<lower_case> to group.cum<camelCase>
May 4, 2023
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8 changes: 4 additions & 4 deletions SUMMARY.md
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Expand Up @@ -212,10 +212,10 @@
* [Groupby.std](api-reference/groupby/groupby.std.md)
* [Groupby.var](api-reference/groupby/groupby.var.md)
* [Groupby.count](api-reference/groupby/groupby.count.md)
* [Groupby.cumsum](api-reference/groupby/groupby.cumsum.md)
* [Groupby.cummax](api-reference/groupby/groupby.cummax.md)
* [Groupby.cummin](api-reference/groupby/groupby.cummin.md)
* [Groupby.cumprod](api-reference/groupby/groupby.cumprod.md)
* [Groupby.cumSum](api-reference/groupby/groupby.cumsum.md)
* [Groupby.cumMax](api-reference/groupby/groupby.cummax.md)
* [Groupby.cumMin](api-reference/groupby/groupby.cummin.md)
* [Groupby.cumProd](api-reference/groupby/groupby.cumprod.md)
* [Groupby.agg](api-reference/groupby/groupby.agg.md)
* [User Guides](examples/README.md)
* [Migrating to the stable version of Danfo.js](examples/migrating-to-the-stable-version-of-danfo.js.md)
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2 changes: 1 addition & 1 deletion api-reference/dataframe/danfo.dataframe.cumsum.md
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Expand Up @@ -64,7 +64,7 @@ data = [[11, 20, 3], [1, 15, 6], [2, 30, 40], [2, 89, 78]]
cols = ["A", "B", "C"]

let df = new dfd.DataFrame(data, { columns: cols })
let new_df = df.cumsum({ axis: 1 })
let new_df = df.cumSum({ axis: 1 })

new_df.print()
```
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2 changes: 1 addition & 1 deletion api-reference/dataframe/danfo.dataframe.groupby.md
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Expand Up @@ -65,7 +65,7 @@ A groupby operation will return a GroupBy class object. You can apply any of the
6. [cumSum](danfo.dataframe.cumsum.md)
7. [cumMax](danfo.dataframe.cummax.md)
8. [cumProd](danfo.dataframe.cumprod.md)
9. [cummin](danfo.dataframe.cummin.md)
9. [cumMin](danfo.dataframe.cummin.md)
10. [max](danfo.dataframe.max.md)
11. [min](danfo.dataframe.min.md)

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2 changes: 1 addition & 1 deletion api-reference/dataframe/dataframe.to_csv.md
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Expand Up @@ -69,7 +69,7 @@ Abs,Count,country code

let df = new dfd.DataFrame(data);

const csv = df.to_csv({ download: false });
const csv = df.toCSV({ download: false });
console.log(csv);
</script>
</body>
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2 changes: 1 addition & 1 deletion api-reference/dataframe/dataframe.to_json.md
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Expand Up @@ -82,7 +82,7 @@ console.log(jsonObjRow);

let df = new dfd.DataFrame(data);

const csv = df.to_csv({ download: false });
const csv = df.toCSV({ download: false });
console.log(csv);
</script>
</body>
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20 changes: 10 additions & 10 deletions api-reference/groupby.md
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Expand Up @@ -37,11 +37,11 @@ description: >-
| [`GroupBy.bfill`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.bfill.html#pandas.core.groupby.GroupBy.bfill)\(\[limit\]\) | Backward fill the values. |
| [`GroupBy.backfill`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.backfill.html#pandas.core.groupby.GroupBy.backfill)\(\[limit\]\) | Backward fill the values. |
| [`GroupBy.count`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.count.html#pandas.core.groupby.GroupBy.count)\(\) | Compute count of group, excluding missing values. |
| [`GroupBy.cumcount`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumcount.html#pandas.core.groupby.GroupBy.cumcount)\(\[ascending\]\) | Number each item in each group from 0 to the length of that group - 1. |
| [`GroupBy.cummax`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cummax.html#pandas.core.groupby.GroupBy.cummax)\(\[axis\]\) | Cumulative max for each group. |
| [`GroupBy.cummin`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cummin.html#pandas.core.groupby.GroupBy.cummin)\(\[axis\]\) | Cumulative min for each group. |
| [`GroupBy.cumprod`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumprod.html#pandas.core.groupby.GroupBy.cumprod)\(\[axis\]\) | Cumulative product for each group. |
| [`GroupBy.cumsum`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumsum.html#pandas.core.groupby.GroupBy.cumsum)\(\[axis\]\) | Cumulative sum for each group. |
| [`GroupBy.cumCount`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumcount.html#pandas.core.groupby.GroupBy.cumcount)\(\[ascending\]\) | Number each item in each group from 0 to the length of that group - 1. |
| [`GroupBy.cumMax`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cummax.html#pandas.core.groupby.GroupBy.cummax)\(\[axis\]\) | Cumulative max for each group. |
| [`GroupBy.cumMin`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cummin.html#pandas.core.groupby.GroupBy.cummin)\(\[axis\]\) | Cumulative min for each group. |
| [`GroupBy.cumProd`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumprod.html#pandas.core.groupby.GroupBy.cumprod)\(\[axis\]\) | Cumulative product for each group. |
| [`GroupBy.cumSum`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.cumsum.html#pandas.core.groupby.GroupBy.cumsum)\(\[axis\]\) | Cumulative sum for each group. |
| [`GroupBy.ffill`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.ffill.html#pandas.core.groupby.GroupBy.ffill)\(\[limit\]\) | Forward fill the values. |
| [`GroupBy.first`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.first.html#pandas.core.groupby.GroupBy.first)\(\[numeric\_only, min\_count\]\) | Compute first of group values. |
| [`GroupBy.head`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.head.html#pandas.core.groupby.GroupBy.head)\(\[n\]\) | Return first n rows of each group. |
Expand Down Expand Up @@ -74,11 +74,11 @@ The following methods are available in both `SeriesGroupBy` and `DataFrameGroupB
| [`DataFrameGroupBy.corr`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.corr.html#pandas.core.groupby.DataFrameGroupBy.corr) | Compute pairwise correlation of columns, excluding NA/null values. |
| [`DataFrameGroupBy.count`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.count.html#pandas.core.groupby.DataFrameGroupBy.count)\(\) | Compute count of group, excluding missing values. |
| [`DataFrameGroupBy.cov`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cov.html#pandas.core.groupby.DataFrameGroupBy.cov) | Compute pairwise covariance of columns, excluding NA/null values. |
| [`DataFrameGroupBy.cumcount`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cumcount.html#pandas.core.groupby.DataFrameGroupBy.cumcount)\(\[ascending\]\) | Number each item in each group from 0 to the length of that group - 1. |
| [`DataFrameGroupBy.cummax`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cummax.html#pandas.core.groupby.DataFrameGroupBy.cummax)\(\[axis\]\) | Cumulative max for each group. |
| [`DataFrameGroupBy.cummin`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cummin.html#pandas.core.groupby.DataFrameGroupBy.cummin)\(\[axis\]\) | Cumulative min for each group. |
| [`DataFrameGroupBy.cumprod`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cumprod.html#pandas.core.groupby.DataFrameGroupBy.cumprod)\(\[axis\]\) | Cumulative product for each group. |
| [`DataFrameGroupBy.cumsum`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cumsum.html#pandas.core.groupby.DataFrameGroupBy.cumsum)\(\[axis\]\) | Cumulative sum for each group. |
| [`DataFrameGroupBy.cumCount`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cumcount.html#pandas.core.groupby.DataFrameGroupBy.cumcount)\(\[ascending\]\) | Number each item in each group from 0 to the length of that group - 1. |
| [`DataFrameGroupBy.cumMax`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cummax.html#pandas.core.groupby.DataFrameGroupBy.cummax)\(\[axis\]\) | Cumulative max for each group. |
| [`DataFrameGroupBy.cumMin`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cummin.html#pandas.core.groupby.DataFrameGroupBy.cummin)\(\[axis\]\) | Cumulative min for each group. |
| [`DataFrameGroupBy.cumProd`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cumprod.html#pandas.core.groupby.DataFrameGroupBy.cumprod)\(\[axis\]\) | Cumulative product for each group. |
| [`DataFrameGroupBy.cumSum`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.cumsum.html#pandas.core.groupby.DataFrameGroupBy.cumsum)\(\[axis\]\) | Cumulative sum for each group. |
| [`DataFrameGroupBy.describe`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.describe.html#pandas.core.groupby.DataFrameGroupBy.describe)\(\*\*kwargs\) | Generate descriptive statistics. |
| [`DataFrameGroupBy.diff`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.diff.html#pandas.core.groupby.DataFrameGroupBy.diff) | First discrete difference of element. |
| [`DataFrameGroupBy.ffill`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.DataFrameGroupBy.ffill.html#pandas.core.groupby.DataFrameGroupBy.ffill)\(\[limit\]\) | Forward fill the values. |
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8 changes: 4 additions & 4 deletions api-reference/groupby/README.md
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Expand Up @@ -21,10 +21,10 @@ description: 'GroupBy objects are returned by groupby calls: danfo.DataFrame.gro
| | |
| :--- | :--- |
| [`GroupBy.count`](groupby.count.md) | Compute count of group, excluding missing values. |
| [`GroupBy.cummax`](groupby.cummax.md) | Cumulative max for each group. |
| [`GroupBy.cummin`](groupby.cummin.md) | Cumulative min for each group. |
| [`GroupBy.cumprod`](groupby.cumprod.md) | Cumulative product for each group. |
| [`GroupBy.cumsum`](groupby.cumsum.md) | Cumulative sum for each group. |
| [`GroupBy.cumMax`](groupby.cummax.md) | Cumulative max for each group. |
| [`GroupBy.cumMin`](groupby.cummin.md) | Cumulative min for each group. |
| [`GroupBy.cumProd`](groupby.cumprod.md) | Cumulative product for each group. |
| [`GroupBy.cumSum`](groupby.cumsum.md) | Cumulative sum for each group. |
| [`GroupBy.max`](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.core.groupby.GroupBy.max.html#pandas.core.groupby.GroupBy.max) | Compute max of group values. |
| [`GroupBy.mean`](groupby.mean.md) | Compute mean of groups, excluding missing values. |
| [`GroupBy.min`](groupby.min.md) | Compute min of group values. |
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4 changes: 2 additions & 2 deletions api-reference/groupby/groupby.cummax.md
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Expand Up @@ -73,7 +73,7 @@ Shape: (5,2)
╚═══╧═══════════════════╧═══════════════════╝
```

Obtain the cumsum for two columns for each group, group by one column
Obtain the cumMax for two columns for each group, group by one column

{% tabs %}
{% tab title="Node" %}
Expand Down Expand Up @@ -134,7 +134,7 @@ grp.col(["C","D"]).cumMax().tail().print()
╚════╧═══════════════════╧═══════════════════╧═══════════════════╝
```

Obtain the cummax for a column for each group, group by two columns
Obtain the cumMax for a column for each group, group by two columns

{% tabs %}
{% tab title="Node" %}
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6 changes: 3 additions & 3 deletions api-reference/groupby/groupby.cummin.md
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Expand Up @@ -4,7 +4,7 @@ description: Obtain the cummulative minimum per groups for each column

# Groupby.cumMin

> danfo.Groupby.**cummin**\(\) \[[source](https://github.com/javascriptdata/danfojs/blob/4993242be7847ba7583dd40ed0188929898b8fd6/src/danfojs-base/aggregators/groupby.ts#L497)\]
> danfo.Groupby.**cumMin**\(\) \[[source](https://github.com/javascriptdata/danfojs/blob/4993242be7847ba7583dd40ed0188929898b8fd6/src/danfojs-base/aggregators/groupby.ts#L497)\]

**Parameters**: None

Expand Down Expand Up @@ -75,7 +75,7 @@ grpColC.cumMin().tail().print()
╚═══╧═══════════════════╧═══════════════════╝
```

Obtain the cummin for two columns for each group, group by one column
Obtain the cumMin for two columns for each group, group by one column

{% tabs %}
{% tab title="Node" %}
Expand Down Expand Up @@ -137,7 +137,7 @@ grpCol.cumMin().tail().print()
╚════╧═══════════════════╧═══════════════════╧═══════════════════╝
```

Obtain the cummin for a column for each group, group by two columns
Obtain the cumMin for a column for each group, group by two columns

{% tabs %}
{% tab title="Node" %}
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8 changes: 4 additions & 4 deletions api-reference/groupby/groupby.cumprod.md
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Expand Up @@ -4,7 +4,7 @@ description: Obtain the cumulative product per group for each column

# Groupby.cumProd

> danfo.Groupby.**cumprod**\(\) \[[source](https://github.com/javascriptdata/danfojs/blob/65f9b3753389b08101d4bb00a2d6488255476aaf/src/danfojs-base/aggregators/groupby.ts#L489)\]
> danfo.Groupby.**cumProd**\(\) \[[source](https://github.com/javascriptdata/danfojs/blob/65f9b3753389b08101d4bb00a2d6488255476aaf/src/danfojs-base/aggregators/groupby.ts#L489)\]

**Parameters**: None

Expand Down Expand Up @@ -74,7 +74,7 @@ grpCol.cumProd().tail().print()
╚═══╧═══════════════════╧═══════════════════╝
```

Obtain the cumprod for two columns for each groups, group by one column
Obtain the cumProd for two columns for each groups, group by one column

{% tabs %}
{% tab title="Node" %}
Expand Down Expand Up @@ -122,7 +122,7 @@ grpCol.cumProd().print()
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
```

Obtain the cumprod for a column for each group, group by two columns
Obtain the cumProd for a column for each group, group by two columns

{% tabs %}
{% tab title="Node" %}
Expand Down Expand Up @@ -170,7 +170,7 @@ grpCol.cumProd().print()
╚════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
```

Obtain the cumprod for two columns for each group, group by two columns
Obtain the cumProd for two columns for each group, group by two columns

{% tabs %}
{% tab title="Node" %}
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4 changes: 2 additions & 2 deletions api-reference/groupby/groupby.cumsum.md
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Expand Up @@ -2,9 +2,9 @@
description: Obtain the cumulative sum per groups for each column
---

# Groupby.cumsum
# Groupby.cumSum

> danfo.Groupby.**cumsum**() \[[source](https://github.com/javascriptdata/danfojs/blob/0d33e344b80a3ed54c91c9393ac3b583d4b0b1a4/src/danfojs-base/aggregators/groupby.ts#L473)]
> danfo.Groupby.**cumSum**() \[[source](https://github.com/javascriptdata/danfojs/blob/0d33e344b80a3ed54c91c9393ac3b583d4b0b1a4/src/danfojs-base/aggregators/groupby.ts#L473)]

**Parameters**: None

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10 changes: 5 additions & 5 deletions api-reference/input-output/README.md
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Expand Up @@ -8,11 +8,11 @@ description: Functions for reading tabular/structured data into DataFrame/Series

| \`\` | |
| ----------------------------------- | -------------------------------------------------------- |
| [`read_csv`](danfo.read_csv.md) | Read a comma-separated values (csv) file into DataFrame. |
| [`readCSV`](danfo.read_csv.md) | Read a comma-separated values (csv) file into DataFrame. |
| [`read_excel`](danfo.read_excel.md) | Read an Excel values (xlsx) file into DataFrame. |
| [`read_json`](danfo.read_json.md) | Read a JSON values (json) file into DataFrame. |
| [to_csv](danfo.to_csv.md) | Writes a DataFrame/Series to CSV file |
| [`readJSON`](danfo.read_json.md) | Read a JSON values (json) file into DataFrame. |
| [toCSV](danfo.to_csv.md) | Writes a DataFrame/Series to CSV file |
| [to_excel](danfo.to_excel.md) | Writes a DataFrame/Series to Excel file |
| [to_json](danfo.to_json.md) | Writes a DataFrame/Series to JSON file |
| [toJSON](danfo.to_json.md) | Writes a DataFrame/Series to JSON file |

Writing to `CSV` and `JSON` can also be done directly from DataFrame or Series objects (e.g. [`DataFrame.to_csv()`](../dataframe/dataframe.to_csv.md))
Writing to `CSV` and `JSON` can also be done directly from DataFrame or Series objects (e.g. [`DataFrame.toCSV()`](../dataframe/dataframe.to_csv.md))
2 changes: 1 addition & 1 deletion api-reference/plotting/violin-plots.md
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Expand Up @@ -122,7 +122,7 @@ export default App;
<div id="plot_div"></div>
<script>

dfd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/titanic.csv")
dfd.readCSV("https://raw.githubusercontent.com/pandas-dev/pandas/master/doc/data/titanic.csv")
.then(df => {

sub_df = df.loc({ columns: ["Age", "Fare"] })
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2 changes: 1 addition & 1 deletion examples/titanic-survival-prediction-using-danfo.js-and-tensorflow.js.md
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Expand Up @@ -77,7 +77,7 @@ async function load_process_data() {
╚═══╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╧═══════════════════╝
```

You wrote an async function because loading a dataset over the internet takes a few seconds depending on your network. Inside the async function, you pass in the url of the titanic dataset to the read\_csv function.
You wrote an async function because loading a dataset over the internet takes a few seconds depending on your network. Inside the async function, you pass in the url of the titanic dataset to the readCSV function.

Next you'll perform some basic data preprocessing. The [ctypes](../api-reference/dataframe/dataframe.dtypes.md) attribute returns the column data types:

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2 changes: 1 addition & 1 deletion getting-started.md
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Expand Up @@ -2368,7 +2368,7 @@ dfd.readCSV("/home/Desktop/titanic.csv")

<script>

dfd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
dfd.readCSV("https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv")
.then(df => {

//do something like display descriptive statistics
Expand Down