From 57f65b30d28322f59be4e25863c9cdf9ca7f6536 Mon Sep 17 00:00:00 2001 From: jinglinpeng Date: Wed, 19 May 2021 12:38:40 -0700 Subject: [PATCH] docs(eda):replace x, y, z with col1, col2, col3 --- docs/source/user_guide/eda/insights.ipynb | 12 ++++---- .../eda/parameter_configurations.ipynb | 6 ++-- docs/source/user_guide/eda/plot.ipynb | 30 +++++++++---------- .../user_guide/eda/plot_correlation.ipynb | 16 +++++----- docs/source/user_guide/eda/plot_missing.ipynb | 18 +++++------ 5 files changed, 41 insertions(+), 41 deletions(-) diff --git a/docs/source/user_guide/eda/insights.ipynb b/docs/source/user_guide/eda/insights.ipynb index b9a63ddc9..9a073c5f6 100644 --- a/docs/source/user_guide/eda/insights.ipynb +++ b/docs/source/user_guide/eda/insights.ipynb @@ -69,7 +69,7 @@ "id": "engaged-prince", "metadata": {}, "source": [ - "If we use plot(df, x) function, we have to click the following buttom:\n", + "If we use `plot(df, col)` function, we have to click the following buttom:\n", "\n", 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)" ] @@ -124,7 +124,7 @@ "id": "fatal-recovery", "metadata": {}, "source": [ - "## The insights provided by plot(df, x) when x is a continues column\n", + "## The insights provided by plot(df, col) when col is a continues column\n", "\n", "Here we give an example to show the insights could be yielded by plot(df, x), when x is a continues column.\n", "\n", @@ -147,7 +147,7 @@ "metadata": {}, "outputs": [], "source": [ - "plot(df, x=\"Age\")" + "plot(df, \"Age\")" ] }, { @@ -155,9 +155,9 @@ "id": "streaming-neutral", "metadata": {}, "source": [ - "## The insights provided by plot(df, x) when x is a nominal column\n", + "## The insights provided by plot(df, col) when col is a nominal column\n", "\n", - "Here we give an example to show the insights could be presented by plot(df, x), when x is a nominal column.\n", + "Here we give an example to show the insights could be presented by plot(df, col), when col is a nominal column.\n", "\n", "| insights | applied plots | type | threshold | discription |\n", "| :---- | :---- | :----| :---- | :---- |\n", @@ -203,4 +203,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/docs/source/user_guide/eda/parameter_configurations.ipynb b/docs/source/user_guide/eda/parameter_configurations.ipynb index db7be4f22..c9967a81a 100644 --- a/docs/source/user_guide/eda/parameter_configurations.ipynb +++ b/docs/source/user_guide/eda/parameter_configurations.ipynb @@ -22,8 +22,8 @@ "\n", "| Global Parameter | Description |\n", "| --- | --- | \n", - "| `width` | Change the plots' width in `plot(df, x)`, `plot(df, x, y)`, `plot(df, x, y, z)`, `plot_correlation()` and `plot_missing()`.\n", - "| `height` | Change the plots' height in `plot(df, x)`, `plot(df, x, y)` and `plot(df, x, y, z)`, `plot_correlation()` and `plot_missing()`.\n", + "| `width` | Change the plots' width in `plot(df, col1)`, `plot(df, col1, col2)`, `plot(df, col1, col2, col3)`, `plot_correlation()` and `plot_missing()`.\n", + "| `height` | Change the plots' height in `plot(df, col1)`, `plot(df, col1, col2)` and `plot(df, col1, col2, col3)`, `plot_correlation()` and `plot_missing()`.\n", "| `bins` | Apply to `bins` for `Histogram`, `KDE Plot`, `Box Plot`, `Word Length`, `Line Chart`, `Spectrum`.\n", "| `ngroups` | Apply to `bars` and `slices` for the `Bar Chart` and `Pie Chart`." ] @@ -207,4 +207,4 @@ }, "nbformat": 4, "nbformat_minor": 2 -} +} \ No newline at end of file diff --git a/docs/source/user_guide/eda/plot.ipynb b/docs/source/user_guide/eda/plot.ipynb index 67d5d00a8..050ca7382 100644 --- a/docs/source/user_guide/eda/plot.ipynb +++ b/docs/source/user_guide/eda/plot.ipynb @@ -21,12 +21,12 @@ "The function `plot()` explores the distributions and statistics of the dataset. It generates a variety of visualizations and statistics which enables the user to achieve a comprehensive understanding of the column distributions and their relationships. The following describes the functionality of `plot()` for a given dataframe `df`.\n", "\n", "1. `plot(df)`: plots the distribution of each column and computes dataset statistics\n", - "2. `plot(df, x)`: plots the distribution of column `x` in various ways, and computes its statistics\n", - "3. `plot(df, x, y)`: generates plots depicting the relationship between columns `x` and `y`\n", + "2. `plot(df, col1)`: plots the distribution of column `col1` in various ways, and computes its statistics\n", + "3. `plot(df, col1, col2)`: generates plots depicting the relationship between columns `col1` and `col2`\n", "\n", "The generated plots are different for numerical, categorical and geography columns. The following table summarizes the output for the different column types.\n", "\n", - "| `x` | `y` | Output |\n", + "| `col1` | `col2` | Output |\n", "| --- | --- | --- |\n", "| None | None | dataset statistics, [histogram](https://www.wikiwand.com/en/Histogram) or [bar chart](https://www.wikiwand.com/en/Bar_chart) for each column |\n", "| Numerical | None | column statistics, histogram, [kde plot](https://www.wikiwand.com/en/Kernel_density_estimation), [qq-normal plot](https://www.wikiwand.com/en/Q%E2%80%93Q_plot), [box plot](https://www.wikiwand.com/en/Box_plot) |\n", @@ -101,11 +101,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Understand a column with `plot(df, x)`\n", + "## Understand a column with `plot(df, col1)`\n", "\n", - "After getting an overview of the dataset, we can thoroughly investigate a column of interest `x` using `plot(df, x)`. The output is of `plot(df, x)` is different for numerical and categorical columns.\n", + "After getting an overview of the dataset, we can thoroughly investigate a column of interest `col1` using `plot(df, col1)`. The output is of `plot(df, col1)` is different for numerical and categorical columns.\n", "\n", - "When `x` is a numerical column, it computes column statistics, and generates a histogram, kde plot, box plot and qq-normal plot:" + "When `col1` is a numerical column, it computes column statistics, and generates a histogram, kde plot, box plot and qq-normal plot:" ] }, { @@ -164,11 +164,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Understand the relationship between two columns with `plot(df, x, y)`\n", + "## Understand the relationship between two columns with `plot(df, col1, col2)`\n", "\n", - "Next, we can explore the relationship between columns `x` and `y` using `plot(df, x, y)`. The output depends on the types of the columns. \n", + "Next, we can explore the relationship between columns `col1` and `col2` using `plot(df, col1, col2)`. The output depends on the types of the columns. \n", "\n", - "When `x` and `y` are both numerical columns, it generates a scatter plot, hexbin plot and box plot:" + "When `col1` and `col2` are both numerical columns, it generates a scatter plot, hexbin plot and box plot:" ] }, { @@ -189,7 +189,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When `x` and `y` are both categorical columns, it plots a nested bar chart, stacked bar chart and heat map:" + "When `col1` and `col2` are both categorical columns, it plots a nested bar chart, stacked bar chart and heat map:" ] }, { @@ -210,7 +210,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "When `x` and `y` are one each of type numerical and categorical, it generates a box plot per category and a multi-line chart:" + "When `col1` and `col2` are one each of type numerical and categorical, it generates a box plot per category and a multi-line chart:" ] }, { @@ -230,7 +230,7 @@ }, { "source": [ - "When `x` and `y` are one each of type geopoint and categorical, or, geography and categorical, it generates a box plot per category and a multi-line chart:" + "When `col1` and `col2` are one each of type geopoint and categorical, or, geography and categorical, it generates a box plot per category and a multi-line chart:" ], "cell_type": "markdown", "metadata": {} @@ -241,7 +241,7 @@ "metadata": {}, "outputs": [], "source": [ - "from dataprep.eda.dtypes import LatLong\n", + "from dataprep.eda.dtypes_v2 import LatLong\n", "covid = load_dataset('covid19')\n", "latlong = LatLong(\"Lat\", \"Long\") # create geopoint type using \"LatLong\" function by inputing two columns names\n", "plot(covid, latlong, \"Country/Region\")\n", @@ -253,7 +253,7 @@ }, { "source": [ - "When `x` and `y` are one each of type geography and numerical, it generates a box plot per category, a multi-line chart and a world map:" + "When `col1` and `col2` are one each of type geography and numerical, it generates a box plot per category, a multi-line chart and a world map:" ], "cell_type": "markdown", "metadata": {} @@ -270,7 +270,7 @@ }, { "source": [ - "When `x` and `y` are one each of type geopoint and numerical, it generates a geo map:" + "When `col1` and `col2` are one each of type geopoint and numerical, it generates a geo map:" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/user_guide/eda/plot_correlation.ipynb b/docs/source/user_guide/eda/plot_correlation.ipynb index 311815f0b..509064181 100644 --- a/docs/source/user_guide/eda/plot_correlation.ipynb +++ b/docs/source/user_guide/eda/plot_correlation.ipynb @@ -16,12 +16,12 @@ "The function `plot_correlation()` explores the correlation between columns in various ways and using multiple correlation metrics. The following describes the functionality of `plot_correlation()` for a given dataframe `df`.\n", "\n", "1. `plot_correlation(df)`: plots correlation matrices (correlations between all pairs of columns)\n", - "2. `plot_correlation(df, x)`: plots the most correlated columns to column `x`\n", - "3. `plot_correlation(df, x, y)`: plots the joint distribution of column `x` and column `y` and computes a regression line\n", + "2. `plot_correlation(df, col1)`: plots the most correlated columns to column `col1`\n", + "3. `plot_correlation(df, col1, col2)`: plots the joint distribution of column `col1` and column `col2` and computes a regression line\n", "\n", - "The following table summarizes the output plots for different settings of `x` and `y`.\n", + "The following table summarizes the output plots for different settings of `col1` and `col2`.\n", "\n", - "| `x` | `y` | Output |\n", + "| `col1` | `col2` | Output |\n", "| --- | --- | --- |\n", "| None | None | *n*\\**n* correlation matrix, computed with [Person](https://www.wikiwand.com/en/Pearson_correlation_coefficien), [Spearman](https://www.wikiwand.com/en/Spearman%27s_rank_correlation_coefficient), and [KendallTau](https://www.wikiwand.com/en/Kendall_rank_correlation_coefficient) correlation coefficients | \n", "| Numerical | None | *n*\\*1 correlation matrix, computed with Pearson, Spearman, and KendallTau correlation coefficients |\n", @@ -86,7 +86,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Find the columns that are most correlated to column `x` with `plot_correlation(df, x)`\n", + "## Find the columns that are most correlated to column `col1` with `plot_correlation(df, col1)`\n", "\n", "After computing the correlation matrices, we can discover how other columns correlate to a specific column `x` using `plot_correlation(df, x)`. This function computes the correlation between column `x` and all other columns (using Pearson, Spearman, and KendallTau correlation coefficients), and sorts them in decreasing order. This enables easy determination of the columns that are most positively and negatively correlated with column `x`. The following shows an example:" ] @@ -109,9 +109,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Explore the correlation between two columns with `plot_correlation(df, x, y)`\n", + "## Explore the correlation between two columns with `plot_correlation(df, col1, col2)`\n", "\n", - "Furthermore, `plot_correlation(df, x, y)` provides detailed analysis of the correlation between two columns `x` and `y`. It plots the joint distribution of the columns `x` and `y` as a scatter plot, as well as a regression line. The following shows an example:" + "Furthermore, `plot_correlation(df, col1, col2)` provides detailed analysis of the correlation between two columns `col1` and `col2`. It plots the joint distribution of the columns `col1` and `col2` as a scatter plot, as well as a regression line. The following shows an example:" ] }, { @@ -193,4 +193,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file diff --git a/docs/source/user_guide/eda/plot_missing.ipynb b/docs/source/user_guide/eda/plot_missing.ipynb index 5c60d748b..0a1196556 100644 --- a/docs/source/user_guide/eda/plot_missing.ipynb +++ b/docs/source/user_guide/eda/plot_missing.ipynb @@ -21,8 +21,8 @@ "The function `plot_missing()` enables thorough analysis of the missing values and their impact on the dataset. The *impact* is the change in the dataset's characteristics (e.g., the histogram of a numerical column or bar chart of a categorical column) after removing the rows with missing values from the dataset. The following describes the functionality of `plot_missing()` for a given dataframe `df`.\n", "\n", "1. `plot_missing(df)`: plots the amount and position of missing values, and their relationship between columns\n", - "2. `plot_missing(df, x)`: plots the impact of the missing values in column `x` on all other columns\n", - "3. `plot_missing(df, x, y)`: plots the impact of the missing values from column `x` on column `y` in various ways.\n", + "2. `plot_missing(df, col1)`: plots the impact of the missing values in column `col1` on all other columns\n", + "3. `plot_missing(df, col1, col2)`: plots the impact of the missing values from column `col1` on column `col2` in various ways.\n", "\n", "Next, we demonstrate the functionality of `plot_missing()`. " ] @@ -95,9 +95,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Understand the *impact* of the missing values in column *x* with `plot_missing(df, x)`\n", + "## Understand the *impact* of the missing values in column *x* with `plot_missing(df, col1)`\n", "\n", - "After getting an overview of the missing values with `plot_missing(df)`, we can analyze the impact of the missing values in a specific column `x` with `plot_missing(df, x)`. The *impact* of the missing values in column `x` is the change in the dataset's characteristics after removing the rows where column `x`'s values are missing. Here, we consider two types of characteristics: the histogram (for numerical columns) and the bar chart (for categorical columns). `plot_missing(df, x)` plots the histogram or bar chart (for appropriate column types) for each column before and after removing the rows that contain missing values in column `x`.\n", + "After getting an overview of the missing values with `plot_missing(df)`, we can analyze the impact of the missing values in a specific column `col1` with `plot_missing(df, col1)`. The *impact* of the missing values in column `col1` is the change in the dataset's characteristics after removing the rows where column `col1`'s values are missing. Here, we consider two types of characteristics: the histogram (for numerical columns) and the bar chart (for categorical columns). `plot_missing(df, col1)` plots the histogram or bar chart (for appropriate column types) for each column before and after removing the rows that contain missing values in column `col1`.\n", "\n", "The following shows an example:" ] @@ -120,12 +120,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Understand the impact of the missing values in column `x` on column `y` with `plot_missing(df, x, y)`\n", + "## Understand the impact of the missing values in column `col1` on column `col2` with `plot_missing(df, col1, col2)`\n", "\n", "\n", - "`plot_missing(df, x)` only displays the frequency distribution of each column before and after removing the rows containing missing values in column `x`. If the user is specifically concerned with the impact of the missing values in one column `x` on another column `y`, she/he can call `plot_missing(df, x, y)`. `plot_missing(df, x, y)` plots the impact of the missing values in column `x` on column `y` in different ways depending on the type of column `y`.\n", + "`plot_missing(df, col1)` only displays the frequency distribution of each column before and after removing the rows containing missing values in column `col1`. If the user is specifically concerned with the impact of the missing values in one column `col1` on another column `col2`, she/he can call `plot_missing(df, col1, col2)`. `plot_missing(df, col1, col2)` plots the impact of the missing values in column `col1` on column `col2` in different ways depending on the type of column `col2`.\n", "\n", - "If `y` is a numerical column, `plot_missing(df, x, y)` shows the impact as a histogram, pdf, cdf, and box plot. The following shows an example:" + "If `col2` is a numerical column, `plot_missing(df, col1, col2)` shows the impact as a histogram, pdf, cdf, and box plot. The following shows an example:" ] }, { @@ -146,7 +146,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If `y` is a categorical column, `plot_missing(df, x, y)` shows the impact as a bar chart. The following shows an example:" + "If `y` is a categorical column, `plot_missing(df, col1, col2)` shows the impact as a bar chart. The following shows an example:" ] }, { @@ -228,4 +228,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file