Initial commit

Author: Parashar7

.gitattributes

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+# Auto detect text files and perform LF normalization
+* text=auto

.idea/.gitignore

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+import pandas as pd
+
+data = pd.read_csv('covid_data.csv')
+print(data)
+print(data.query('new_cases < 0'))
+
+data[data.new_cases < 0] = 0
+print(data)
+
+df = data.fillna(0)  # fills nan value with 0 to whole file
+#print(df)
+df.to_csv('df.csv', index=None)
+df.to_csv('df_with_index.csv')
+# data['iso_code'] = data['iso_code'].fillna(0)  # fills nan value with 0 in a particular column
+# print(data)
+# print(data.info())
+
+# final_df = df[['iso_code', 'location', 'date', 'total_cases', 'new_cases', 'total_deaths', 'positive_rate']]
+# print(final_df)

.idea/Exercise_Matplotlib.iml

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+# Exercise_Matplotlib
+ Some initial exercises on Matplotlib

.idea/inspectionProfiles/Project_Default.xml

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+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+
+covid_df = pd.read_csv('covid_data.csv')
+covid_df['date'] = pd.to_datetime(covid_df.date)
+covid_df['month'] = pd.DatetimeIndex(covid_df.date).month
+covid_df['weekday'] = pd.DatetimeIndex(covid_df.date).week
+covid_df['day'] = pd.DatetimeIndex(covid_df.date).day
+covid_df['year'] = pd.DatetimeIndex(covid_df.date).year
+print(covid_df.info())
+
+new_df = covid_df[
+    ['iso_code', 'date', 'total_cases', 'new_cases', 'total_deaths', 'positive_rate', 'female_smokers', 'male_smokers',
+     'month', 'weekday', 'day', 'new_deaths', 'year']]
+print(new_df.info())
+final_df = new_df.query("iso_code==['IND', 'USA', 'NZL', 'BRA']")
+print(final_df.nunique())
+plt.figure(figsize=(12, 6))
+sns.set_style('darkgrid')
+plt.title('Covid Cases of India')
+plt.xlabel('Month', size=14)
+plt.ylabel('New Cases', size=14)
+# plt.bar('month', 'new_cases', data=final_df)
+
+
+# sns.barplot(x='month', y='new_deaths', data=final_df, hue='iso_code', ci=68, palette='Blues_d', dodge=True,
+#           saturation=60)
+
+
+# 1.Catplot
+# sns.catplot(x='month', y='new_deaths', data=final_df, ci=68, palette='Blues_d', dodge=True,
+#            saturation=60, col='iso_code', kind='bar')
+
+
+# 2.Countplot
+# sns.countplot(x='iso_code', data=final_df, palette='Set3')
+
+
+# 3.Pointplot
+# sns.pointplot(x='month', y='new_cases', data=final_df.query("year==2020"), hue='iso_code', palette='Set2',
+#     linestyles=['-', '--', '-.'],dodge=False, join=True)
+
+
+sns.catplot(x='month', y='new_cases', data=final_df.query("year==2020"), dodge=True,
+            col='iso_code', height=5, aspect=0.4, kind='point', hue='day')
+plt.show()

.idea/inspectionProfiles/profiles_settings.xml

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+import matplotlib.pyplot as plt
+import seaborn as sns
+import numpy as np
+
+car_crash = sns.load_dataset('car_crashes')
+# print(car_crash)
+print(car_crash, car_crash.info(), car_crash.describe(), car_crash.nunique())
+plt.figure(figsize=(20, 15))
+sns.set_style('darkgrid')
+plt.title('Car Crash')
+# plt.plot('alcohol', 'o-b', data=car_crash)
+# plt.plot('speeding', 'x--r', data=car_crash)
+# sns.lineplot('alcohol', 'speeding', data=car_crash)
+# plt.legend(['Speeding', 'Alcohol'])
+# plt.xlabel('Value')
+# plt.ylabel('Instances')
+
+
+# sns.scatterplot('speeding', 'alcohol', data=car_crash, hue='not_distracted', s=100)
+# plt.hist('speeding', data=car_crash)
+# plt.hist('alcohol', data=car_crash)
+# plt.hist(['speeding', 'alcohol'], bottom='speeding', data=car_crash,stacked=True)
+# plt.legend(['Alcohol', 'Speeding'])
+# sns.barplot('total', 'alcohol', data=car_crash.loc[20:50+1])
+# plt.bar('total', 'alcohol', data=car_crash.loc[20:50+1])
+# plt.hist('not_distracted', data=car_crash)
+# plt.plot('total', 'alcohol', 's--b', data=car_crash)
+#sns.histplot(x='alcohol', kde=True, data=car_crash)
+#sns.histplot(data=car_crash, x='total', stat='probability', discrete=True)
+#sns.histplot(data=car_crash, x='speeding', log_scale=True, fill=False, element='step')
+#sns.histplot(car_crash, x='alcohol', y='speeding')
+sns.histplot(car_crash, x='alcohol', element='poly')
+#sns.histplot(data=car_crash, x='speeding')
+sns.set_color_codes('muted')
+sns.barplot(x="abbrev", y="total", data=car_crash,
+            label="Total", color="b")
+
+plt.show()

.idea/misc.xml

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+import matplotlib.pyplot as plt
+import seaborn as sns
+import pandas as pd
+
+df = sns.load_dataset('tips')
+sns.catplot(x="sex", y="total_bill",
+            hue="smoker", col="time",
+            data=df, kind="point",
+            dodge=True,
+            height=4, aspect=1)
+plt.show()