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R visualisation better

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This commit is contained in:
Weaponised Autism
2023-03-28 20:41:46 +02:00
parent ca748eb57e
commit 300ce67b60
2 changed files with 27 additions and 17 deletions
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19
py_scripts/engineer.py
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@@ -5,19 +5,30 @@ import mining_hq
from numpy import count_nonzero from numpy import count_nonzero
sns.set() sns.set()
plt.xticks(rotation = 90)
games_pre = mining_hq.games_sales_split_pre games_pre = mining_hq.games_sales_split_pre
games_dur = mining_hq.games_sales_split_dur games_dur = mining_hq.games_sales_split_dur
games_pos = mining_hq.games_sales_split_pos games_pos = mining_hq.games_sales_split_pos
games_fig_pre = sns.barplot(data = games_pre, x = "Year", y = "NA_Sales", estimator = count_nonzero) crime_US = mining_hq.crime_US_intersect
crime_CA = mining_hq.crime_CA_intersect
plt.xticks(rotation = 90)
games_fig_pre = sns.histplot(data = games_pre, x = "Year", palette = sns.color_palette("flare"), kde = True)
plt.show() plt.show()
plt.xticks(rotation = 90) plt.xticks(rotation = 90)
games_fig_dur = sns.barplot(data = games_dur, x = "Year", y = "NA_Sales", estimator = count_nonzero) games_fig2_pre = sns.histplot(data = games_pre, x = "Year", hue = "Genre", multiple = "stack", shrink = 0.65)
plt.show() plt.show()
plt.xticks(rotation = 90) plt.xticks(rotation = 90)
games_fig_pos = sns.barplot(data = games_pos, x = "Year", y = "NA_Sales", estimator = count_nonzero) games_fig_dur = sns.barplot(data = games_dur, x = "Year", y = "NA_Sales")
plt.show()
plt.xticks(rotation = 90)
games_fig_pos = sns.barplot(data = games_pos, x = "Year", y = "NA_Sales")
plt.show()
plt.xticks(rotation = 90)
crime_CA_fig = sns.barplot(data = crime_CA, x = "year", y = "incidents")
plt.show() plt.show()

25
py_scripts/mining_hq.py
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@@ -3,8 +3,7 @@
import pandas as pd import pandas as pd
import numpy as np import numpy as np
import seaborn as sns import seaborn as sns
import digger import digger, gunner
import gunner
# Instantiating globals to be used in other files # Instantiating globals to be used in other files
global games_merged_dat global games_merged_dat
@@ -24,7 +23,7 @@ games_review_final = digger.slice_column(games_review, "GameName", "(Import)")
games_merged_dat = digger.write_joined_df(games_sales, games_review_final) games_merged_dat = digger.write_joined_df(games_sales, games_review_final)
# Acquisition of Merged dataset # Acquisition of Merged dataset
print(games_merged_dat.isnull()) print(games_merged_dat.count())
games_merged_dat.to_csv("datasets/videogames/games_merged.csv") games_merged_dat.to_csv("datasets/videogames/games_merged.csv")
# Loading Crime Datasets # Loading Crime Datasets
@@ -70,22 +69,22 @@ games_merged_dat = gunner.drop_kick(NA_col_list, games_merged_dat)
sale_tri_split = gunner.trisect_by_year(games_merged_dat, 'Year', year_interval) sale_tri_split = gunner.trisect_by_year(games_merged_dat, 'Year', year_interval)
game_sales_split_pre = sale_tri_split[0] games_sales_split_pre = sale_tri_split[0]
game_sales_split_dur = sale_tri_split[1] games_sales_split_dur = sale_tri_split[1]
game_sales_split_pos = sale_tri_split[2] games_sales_split_pos = sale_tri_split[2]
# Displaying Acquired Data # Displaying Acquired Data
print("Acquired Datasets:\n") print("Acquired Datasets:\n")
print(game_sales_split_pre.head(5), print(games_sales_split_pre.head(5),
game_sales_split_dur.head(5), games_sales_split_dur.head(5),
game_sales_split_pos.head(5)) games_sales_split_pos.head(5))
print("Dataset Info:\n") print("Dataset Info:\n")
game_sales_split_pre.info() games_sales_split_pre.info()
game_sales_split_dur.info() games_sales_split_dur.info()
game_sales_split_pos.info() games_sales_split_pos.info()
print(game_sales_split_dur.describe()) print(games_sales_split_dur.describe())
# Required to use binning for cleaning, idk # Required to use binning for cleaning, idk
# https://towardsdatascience.com/data-preprocessing-with-python-pandas-part-5-binning-c5bd5fd1b950 # https://towardsdatascience.com/data-preprocessing-with-python-pandas-part-5-binning-c5bd5fd1b950