# Election Result Graphs
With **ElectionResultGraphs** you can create either four different graph types or all graphs as one chart for displaying the result of a election based on a csv-inputfile.
As input a `csv-file` needs to be provided. In the initialization the data will be processed as `pandas` `dataframe` and graphs from `plotly` can be exported as image-files.
```
pip install electionGraphs
```
## Features
* data from the csv-file will be processed and more data points will be calculated (initialization)
* a `.getGraph()` function can create different types of graphs
* a bar graph showing the results in relative votes can be displayed
* a bar graph showing a comparision of relative votes to last voting period
* a pie chart shows the seat allocation of the parliament
* a stacked bar chart shows possible coalitions
* `.createOnePager()` creates all graph options and combines them into one chart
### Seat Allocation (Parliament)
* seats of a parliament will be allocated based on relative voting results
* a threshold is implemented, so that a party needs to reach a certain relative voting result in order to be considered in the seat allocation (this feature can be adjusted by parameters)
* a list of parties can be excluded from the seat allocation (e.g. a "other" category)
* for the seat allocation the relative results (excluding parties that are defined by the `excludeParties` parameters or by the `percentageLimit`) will be multiplied by `parliamentSeats`, the result will be floored. Not allocated seats will then be allocated each party ordered by the highest modulus (decimal rest from the division) until all seats are allocated
### Coalitions
* coalitions will be calculated based on total seats. The political spectrum will be considered as a threshold
* for every possible coaltion the distance based on the delivered information of `columnSpectrum` will be calculated. Reaches the overall distance the `thresholdPoliticalDistance` the coalition will not be included in the final graph
## Disclaimer
*This tool is loosly based on german elections. Some rulesets may differ from the reality (e.g. not considering first vote, second vote). This is just a hobby project for practicing data processing and data visualization.*
# Example Graph
## One Pager with multiple different graphs
## Single chart of bar graph
# VotingGraphs
For the initialization the mandatory parameters must be given.
```python
from electionGraphs import electionGraphs
# example for instantiating the class
eg = electionGraphs.ElectionGraphs(
"exampleData.csv",
"YEAR",
"VOTINGS",
"PARTY_SHORT",
"PARTY_SPEC",
"PARTY_COLOR",
)
```
After initialization different aspects of logic and styling can be configured by setting the optional parameters:
```python
votingGraphs.titleMain = "My other Title"
votingGraphs.colors["background"] = "#000000"
votingGraphs.fontsize["titleMain"] = 58
```
## mandatory parameters/attributes
`csvFile` = *(str)* csv file to read in -- [For example of the structure of csv see example file](data/exampleData.csv)
`columnYear` = *(str)* name of column in csv in which the year is recorded
`columnVotings` = *(str)* name of column in csv in which the absolute number of votings per party is recorded
`columnParty` = *(str)* name of column in csv in which party name is recorded
`columnSpectrum` = *(str)* name of column in csv in which the political orientation of the party is recorded
`columnColor` = *(str)* name of column in csv in which the color code of the party is recorded
## optional parameters/attributes
`colors` = *(dict{str:str})* hex-color for different elements of the graphs. The value must be a *str* of hex-color (e.g. `"#F2EAD3"`) with following keys:
* `background`
* `diagram`
* `title`
* `subtitle`
* `yaxis`
* `xaxis`
* `grid`
* `values`
* `threshold`
`excludeParties` = *(list[str], default = ["Sonstiges", "Sonstige", "Other"])* list of party names which will not be included in the seat allocation
`filenameBarCompare` = *(str, default = "barDifference.png")* filename of export graph. Must be a .png file
`filenameBarResult` = *(str, default = "barResult.png")* filename of export graph. Must be a .png file
`filenameBarCoalitions` = *(str, default = "barCoalition.png")* filename of export graph. Must be a .png file
`filenameOnePager` = *(str, default = "ElectionResults")* filename of export graph. Must be a .png file
`filenamePieParliament` = *(str, default = "pieParliament.png")* filename of export graph. Must be a .png file
`fontfamily` = *(str, default = "Futura")* font family of all graphs
`fontsize` = *(dict{str:int})* font sizes of different elements. Each key represent a text type with following options:
`title`
`subtitle`
`values`
`yaxis`
`xaxis`
`outputfolder` = *(str, default = "output")* folder in which all output images will be saved
`parliamentSeats` = *(int, default = 120)* number of seats in parliament. This parameter needs be defined at the initialization, otherwise some graphs will not display the seats correctly
`percentageLimit` = *(int, default = 5)* threshold at which a party will not be considered when calculting seat distribution
`thresholdPolitcalDistance` = *(int, default = 300)* defines a threshold for displaying possible coalitions. When the politcal distance in a mathmatical possible coalition is too high, it can be filtered
`titleBarCoalitions` = *(str, default="Koalitionen")* adjusts title of bar graph with possible coalitions
`titleBarCompare` = *(str, default="Veränderung der Wählerstimmen")* adjusts title of bar graph with comparision to last voting
`titleBarResult` = *(str, default="Wählerstimmen")* adjusts title of bar graph with results
`titleMain` = *(str, default="Wahlergebnisse")* adjusts main title of one pager
`titlePieParliament` = *(str, default="Sitzverteilung")* adjusts title of parliament graph
`seperator` = *(str, default = ";")* type of seperator in csv
`subtitleBarCoalitions`= *(str, default="Anzahl Sitze für mögliche Koalitionen")* adjusts a subtitle of corresponding graph
`subtitleBarCompare` = *(str, default="Prozentpunkte im Vergleich zur letzten Wahl")* adjusts a subtitle of corresponding graph
`subtitleBarResult` = *(str, default="Anteil der Wählerstimmen in Prozent")* adjusts a subtitle of corresponding graph
`subtitlePieParliament` = *(str, default="Anzahl Sitze im Parlament")* adjusts a subtitle of corresponding graph
# function: getGraph
Creates a png-file of a specific chart type for the provided data
```python
# example for creating a graph
electionGraphs.getGraph(2021, type="BAR_DIFFERENCE")
```
## mandatory parameters/attributes
`year` = *(int)* year of data
`type` = *(str, specific options)* type of graph:
* `"BAR_RESULT"` = bar graph with basic results
* `"BAR_COMPARE"` = bar graph with comparison to last voting year
* `"PIE_PARLIAMENT"` = pie chart for seat distribution in a parliament
* `"BAR_COALITIONS"` = stacked bar chart with possible coalitions
## optional parameters/attributes
Many of the optional parameters/attributes of the main class **VotingGraphs** will be applied to the charts (e.g. titles, colors, fonts, etc.)
# function: createOnePager
Creates a png-file with all four chart types arranged
```python
# example for creating a one pager
electionGraphs.createOnePager()
```
## optional parameters/attributes
`year` = *(int)* year of data, if left empty all possible years will be processed and for each year a file will be created
Many of the optional parameters/attributes of the main class **VotingGraphs** will be applied to the charts (e.g. titles, colors, fonts, etc.)
# Dependencies
* pandas
* plotly
* kaleido
* pillow
Raw data
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"description": "# Election Result Graphs\nWith **ElectionResultGraphs** you can create either four different graph types or all graphs as one chart for displaying the result of a election based on a csv-inputfile.\nAs input a `csv-file` needs to be provided. In the initialization the data will be processed as `pandas` `dataframe` and graphs from `plotly` can be exported as image-files.\n\n```\npip install electionGraphs\n```\n\n## Features\n* data from the csv-file will be processed and more data points will be calculated (initialization)\n* a `.getGraph()` function can create different types of graphs\n* a bar graph showing the results in relative votes can be displayed\n* a bar graph showing a comparision of relative votes to last voting period\n* a pie chart shows the seat allocation of the parliament\n* a stacked bar chart shows possible coalitions\n* `.createOnePager()` creates all graph options and combines them into one chart\n\n### Seat Allocation (Parliament)\n* seats of a parliament will be allocated based on relative voting results\n* a threshold is implemented, so that a party needs to reach a certain relative voting result in order to be considered in the seat allocation (this feature can be adjusted by parameters)\n* a list of parties can be excluded from the seat allocation (e.g. a \"other\" category)\n* for the seat allocation the relative results (excluding parties that are defined by the `excludeParties` parameters or by the `percentageLimit`) will be multiplied by `parliamentSeats`, the result will be floored. Not allocated seats will then be allocated each party ordered by the highest modulus (decimal rest from the division) until all seats are allocated\n\n### Coalitions\n* coalitions will be calculated based on total seats. The political spectrum will be considered as a threshold\n* for every possible coaltion the distance based on the delivered information of `columnSpectrum` will be calculated. Reaches the overall distance the `thresholdPoliticalDistance` the coalition will not be included in the final graph\n\n\n## Disclaimer\n*This tool is loosly based on german elections. Some rulesets may differ from the reality (e.g. not considering first vote, second vote). This is just a hobby project for practicing data processing and data visualization.*\n\n# Example Graph\n## One Pager with multiple different graphs\n![Example One Pager](https://github.com/ricochan/electionGraphs/blob/main/electionGraphs/output/ElectionResults_2017.png \"Example One Pager\")\n\n## Single chart of bar graph\n![Example Bar Result](https://github.com/ricochan/electionGraphs/blob/main/electionGraphs/output/barResult.png \"Example Bar Result\")\n\n# VotingGraphs\n\nFor the initialization the mandatory parameters must be given.\n\n```python\nfrom electionGraphs import electionGraphs\n\n# example for instantiating the class\n\neg = electionGraphs.ElectionGraphs(\n \"exampleData.csv\",\n \"YEAR\",\n \"VOTINGS\",\n \"PARTY_SHORT\",\n \"PARTY_SPEC\",\n \"PARTY_COLOR\",\n)\n```\n\nAfter initialization different aspects of logic and styling can be configured by setting the optional parameters:\n\n```python\nvotingGraphs.titleMain = \"My other Title\"\nvotingGraphs.colors[\"background\"] = \"#000000\"\nvotingGraphs.fontsize[\"titleMain\"] = 58\n```\n\n## mandatory parameters/attributes\n\n`csvFile` = *(str)* csv file to read in -- [For example of the structure of csv see example file](data/exampleData.csv)\n\n`columnYear` = *(str)* name of column in csv in which the year is recorded\n\n`columnVotings` = *(str)* name of column in csv in which the absolute number of votings per party is recorded\n\n`columnParty` = *(str)* name of column in csv in which party name is recorded \n\n`columnSpectrum` = *(str)* name of column in csv in which the political orientation of the party is recorded\n\n`columnColor` = *(str)* name of column in csv in which the color code of the party is recorded\n\n## optional parameters/attributes\n\n`colors` = *(dict{str:str})* hex-color for different elements of the graphs. The value must be a *str* of hex-color (e.g. `\"#F2EAD3\"`) with following keys: \n * `background`\n * `diagram`\n * `title`\n * `subtitle` \n * `yaxis` \n * `xaxis` \n * `grid` \n * `values` \n * `threshold` \n\n`excludeParties` = *(list[str], default = [\"Sonstiges\", \"Sonstige\", \"Other\"])* list of party names which will not be included in the seat allocation\n\n`filenameBarCompare` = *(str, default = \"barDifference.png\")* filename of export graph. Must be a .png file\n\n`filenameBarResult` = *(str, default = \"barResult.png\")* filename of export graph. Must be a .png file\n\n`filenameBarCoalitions` = *(str, default = \"barCoalition.png\")* filename of export graph. Must be a .png file\n\n`filenameOnePager` = *(str, default = \"ElectionResults\")* filename of export graph. Must be a .png file\n\n`filenamePieParliament` = *(str, default = \"pieParliament.png\")* filename of export graph. Must be a .png file\n\n`fontfamily` = *(str, default = \"Futura\")* font family of all graphs\n\n`fontsize` = *(dict{str:int})* font sizes of different elements. Each key represent a text type with following options: \n `title`\n `subtitle`\n `values`\n `yaxis`\n `xaxis`\n\n`outputfolder` = *(str, default = \"output\")* folder in which all output images will be saved\n\n`parliamentSeats` = *(int, default = 120)* number of seats in parliament. This parameter needs be defined at the initialization, otherwise some graphs will not display the seats correctly\n\n`percentageLimit` = *(int, default = 5)* threshold at which a party will not be considered when calculting seat distribution\n\n`thresholdPolitcalDistance` = *(int, default = 300)* defines a threshold for displaying possible coalitions. When the politcal distance in a mathmatical possible coalition is too high, it can be filtered\n\n`titleBarCoalitions` = *(str, default=\"Koalitionen\")* adjusts title of bar graph with possible coalitions\n\n`titleBarCompare` = *(str, default=\"Ver\u00e4nderung der W\u00e4hlerstimmen\")* adjusts title of bar graph with comparision to last voting\n\n`titleBarResult` = *(str, default=\"W\u00e4hlerstimmen\")* adjusts title of bar graph with results\n\n`titleMain` = *(str, default=\"Wahlergebnisse\")* adjusts main title of one pager\n\n`titlePieParliament` = *(str, default=\"Sitzverteilung\")* adjusts title of parliament graph\n\n`seperator` = *(str, default = \";\")* type of seperator in csv\n\n`subtitleBarCoalitions`= *(str, default=\"Anzahl Sitze f\u00fcr m\u00f6gliche Koalitionen\")* adjusts a subtitle of corresponding graph\n\n`subtitleBarCompare` = *(str, default=\"Prozentpunkte im Vergleich zur letzten Wahl\")* adjusts a subtitle of corresponding graph\n\n`subtitleBarResult` = *(str, default=\"Anteil der W\u00e4hlerstimmen in Prozent\")* adjusts a subtitle of corresponding graph\n\n`subtitlePieParliament` = *(str, default=\"Anzahl Sitze im Parlament\")* adjusts a subtitle of corresponding graph\n\n\n# function: getGraph\nCreates a png-file of a specific chart type for the provided data\n\n```python\n# example for creating a graph\nelectionGraphs.getGraph(2021, type=\"BAR_DIFFERENCE\")\n```\n## mandatory parameters/attributes\n`year` = *(int)* year of data\n\n`type` = *(str, specific options)* type of graph: \n\n * `\"BAR_RESULT\"` = bar graph with basic results\n\n * `\"BAR_COMPARE\"` = bar graph with comparison to last voting year\n\n * `\"PIE_PARLIAMENT\"` = pie chart for seat distribution in a parliament\n\n * `\"BAR_COALITIONS\"` = stacked bar chart with possible coalitions\n\n## optional parameters/attributes\nMany of the optional parameters/attributes of the main class **VotingGraphs** will be applied to the charts (e.g. titles, colors, fonts, etc.)\n\n# function: createOnePager\nCreates a png-file with all four chart types arranged\n\n```python\n# example for creating a one pager\nelectionGraphs.createOnePager()\n```\n## optional parameters/attributes\n`year` = *(int)* year of data, if left empty all possible years will be processed and for each year a file will be created\n\nMany of the optional parameters/attributes of the main class **VotingGraphs** will be applied to the charts (e.g. titles, colors, fonts, etc.)\n\n# Dependencies\n* pandas\n* plotly\n* kaleido\n* pillow\n\n",
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