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27 changes: 19 additions & 8 deletions notebooks/1_table_oriented.ipynb
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Expand Up @@ -134,14 +134,25 @@
"source": [
"A `DataFrame` is a 2-dimensional data structure that can store data of different types (including characters, integers, floating point values, categorical data and more) in columns. It is similar to a spreadsheet, a SQL table or the `data.frame` in R. \n",
"\n",
"- The table has 3 columns, each of them with a column label. The column labels are respectively `Name`, `Age` and `Sex`.\n",
"- The table above has 3 columns, each of them with a column label. The column labels are `Name`, `Age` and `Sex`, respectively.\n",
"- The column `Name` consists of textual data with each value a string, the column `Age` are numbers and the column `Sex` is textual data.\n",
"\n",
"In spreadsheet software, the table representation of our data would look very similar:\n",
"\n",
"![](../schemas/01_table_spreadsheet.png)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<div class=\"alert alert-info\">\n",
" \n",
"__Note__: You probably do not want to manually input the data of a DataFrame! In most situations, data stored in a file format are the starting point of an analysis. We will get to that later!\n",
"\n",
"</div>"
]
},
{
"cell_type": "markdown",
"metadata": {},
Expand Down Expand Up @@ -199,7 +210,7 @@
"source": [
"<div class=\"alert alert-info\">\n",
" \n",
"If you are familiar to Python :ref:`dictionaries <python:tut-dictionaries>`, the selection of a single column is very similar to selection of dictionary values based on the key.\n",
"If you are familiar to Python :ref:`dictionaries <python:tut-dictionaries>`, the selection of a single column is very similar to the selection of dictionary values based on the key.\n",
"\n",
"</div>"
]
Expand Down Expand Up @@ -287,7 +298,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Or to the `Series`:"
"Or on the `Series`:"
]
},
{
Expand All @@ -314,7 +325,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"As illustrated by the `max()` method, you can _do_ things with a `DataFrame` or `Series`. Pandas provides a lot of functionalities each of them a _method_ you can apply to a `DataFrame` or `Series`. As methods are functions, do not forget to use parentheses `()`."
"As illustrated by the `max()` method, you can _do_ things with a `DataFrame` or `Series`. Pandas provides a lot of functionality for working with `DataFrame` or `Series`, often defined as methods on those objects. As methods are functions, do not forget to use parentheses `()`."
]
},
{
Expand Down Expand Up @@ -415,7 +426,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"The `describe` method provides quick overview of the numerical data in a `DataFrame`. As the `Name` and `Sex` columns are textual data, these are by default not taken into account by the `describe` method. Many pandas operations return a `DataFrame` or a `Series`. The `describe` method is an example of a pandas operation returning a pandas `Series`.\n",
"The `describe` method provides quick overview of the numerical data in a `DataFrame`. As the `Name` and `Sex` columns are textual data, these are by default not taken into account by the `describe` method. Many pandas operations return a `DataFrame` or a `Series`. The `describe` method is an example of a pandas operation returning a pandas `DataFrame`.\n",
"\n",
"\n",
"__To user guide:__ check more options on `describe` :ref:`basics.describe`"
Expand All @@ -438,10 +449,10 @@
"source": [
"## REMEMBER\n",
"\n",
"- Import the package, aka `import Pandas as pd`\n",
"- Import the package, aka `import pandas as pd`\n",
"- A table of data is stored as a pandas `DataFrame`\n",
"- Each column in a `DataFrame` is a `Series`\n",
"- You can do things by applying a method to a `DataFrame` or `Series`"
"- You can do things by calling a method on a `DataFrame` or `Series`"
]
},
{
Expand Down Expand Up @@ -472,5 +483,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}
14 changes: 7 additions & 7 deletions notebooks/2_read_write.ipynb
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Expand Up @@ -17,14 +17,14 @@
" \n",
"This tutorial uses the titanic data set, stored as CSV. The data consists of the following data columns:\n",
"\n",
"- PassengerId: Id of every passenger.\n",
"- Survived: This feature have value 0 and 1. 0 for not survived and 1 for survived.\n",
"- PassengerId: ID of every passenger.\n",
"- Survived: This feature has value 0 and 1. 0 for not survived and 1 for survived.\n",
"- Pclass: There are 3 classes: Class 1, Class 2 and Class 3.\n",
"- Name: Name of passenger.\n",
"- Sex: Gender of passenger.\n",
"- Age: Age of passenger.\n",
"- SibSp: Indication that passenger have siblings and spouse.\n",
"- Parch: Whether a passenger is alone or have family.\n",
"- Parch: Whether a passenger is alone or has family.\n",
"- Ticket: Ticket number of passenger.\n",
"- Fare: Indicating the fare.\n",
"- Cabin: The cabin of passenger.\n",
Expand Down Expand Up @@ -561,7 +561,7 @@
"source": [
"<div class=\"alert alert-info\">\n",
" \n",
"__Note__: Interested in the last N rows instead? Pandas also provides a `tail` method. For example, `titanic.tail(10)` will return the last 10 rows of the DataFrame.\n",
"__Note__: Interested in the last N rows instead? Pandas also provides a `tail()` method. For example, `titanic.tail(10)` will return the last 10 rows of the DataFrame.\n",
"\n",
"</div>"
]
Expand All @@ -570,7 +570,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"A check on how Pandas interpreted each of the column data types can be done by requesting the Pandas `dtypes` attribute:"
"A check on how Pandas interpreted each of the column data types can be done by requesting the `dtypes` attribute:"
]
},
{
Expand Down Expand Up @@ -643,7 +643,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Whereas `read_*` fucntions are used to read data to Pandas, the `to_*` methods are used to store data. The `to_excel` method stores the data as an excel file. In the example here, the `sheet_name` is named _passengers_ instead of the default _Sheet1_. By setting `index=False` the row index labels are not saved in the spreadsheet."
"Whereas `read_*` functions are used to read data to Pandas, the `to_*` methods are used to store data. The `to_excel` method stores the data as an excel file. In the example here, the `sheet_name` is named _passengers_ instead of the default _Sheet1_. By setting `index=False` the row index labels are not saved in the spreadsheet."
]
},
{
Expand Down Expand Up @@ -908,5 +908,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}
13 changes: 9 additions & 4 deletions notebooks/3_subset_data.ipynb
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Expand Up @@ -292,7 +292,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"`shape` is an attribute (remember [previous tutorial](./2_read_write.ipynb), no parantheses for attributes) of a pandas `Series` and `DataFrame` containing the number of rows and columns: _(nrows, ncolumns)_. A pandas Series is 1-dimensional and only the number of rows is returned."
"`shape` is an attribute (remember [previous tutorial](./2_read_write.ipynb), no parentheses for attributes) of a pandas `Series` and `DataFrame` containing the number of rows and columns: _(nrows, ncolumns)_. A pandas Series is 1-dimensional and only the number of rows is returned."
]
},
{
Expand Down Expand Up @@ -389,7 +389,12 @@
"\n",
"<div class=\"alert alert-info\">\n",
" \n",
"__Note:__ The inner square brackets define a :ref:`Python list <python:tut-morelists>` with column names, whereas the outer brackets are used to select the data from a pandas `DataFrame` as seen in the previous example.\n",
"__Note:__ The inner square brackets define a :ref:`Python list <python:tut-morelists>` with column names, whereas the outer brackets are used to select the data from a pandas `DataFrame`. The previous example can therefore also be written as:\n",
"\n",
"```python\n",
"columns_to_select = [\"Age\", \"Sex\"]\n",
"titanic[columns_to_select]\n",
"```\n",
"\n",
"</div>"
]
Expand Down Expand Up @@ -1020,7 +1025,7 @@
"source": [
"<div class=\"alert alert-info\">\n",
" \n",
"__Note:__ When combining multiple conditional statements, each condition must be surrounded by parentheses `()`. Moreover, you can not use `or`/`and` but need to use the `or` operator `|` and the `and` operator `&`.\n",
"__Note:__ When combining multiple conditional statements, each condition must be surrounded by parentheses `()`. Moreover, you can not use `or`/`and` but need to use the \"or\" operator `|` and the \"and\" operator `&`.\n",
"\n",
"</div>"
]
Expand Down Expand Up @@ -1674,5 +1679,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}
2 changes: 1 addition & 1 deletion notebooks/4_plotting.ipynb
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Expand Up @@ -493,5 +493,5 @@
}
},
"nbformat": 4,
"nbformat_minor": 2
"nbformat_minor": 4
}