groupby exercises added.

Author: LimaOscarData

Groupby_in_pandas.py

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+import pandas as pd
+import matplotlib.pyplot as plt
+
+drinks = pd.read_csv('http://bit.ly/drinksbycountry')
+print(drinks.head(), "\n"*3) # I used *3 for seperating head of dataset.
+
+#        country  beer_servings  ...  total_litres_of_pure_alcohol  continent
+# 0  Afghanistan              0  ...                           0.0       Asia
+# 1      Albania             89  ...                           4.9     Europe
+# 2      Algeria             25  ...                           0.7     Africa
+# 3      Andorra            245  ...                          12.4     Europe
+# 4       Angola            217  ...                           5.9     Africa
+#
+# [5 rows x 6 columns]
+
+###########################################################
+#  lets calculate the mean beer service of entire dataset
+
+# print(drinks['beer_servings'].mean())
+# or you can do that :
+# print(drinks.beer_servings.mean()) but do not use it. because:
+# 1 ) With brackets you can select multiple columns (e.g., df[['col1', 'col2']]) or add
+# a new column (df['newcol'] = ...), which can't be done with dot access.
+# 2 ) accessing a single column with a simple name, but you can do more with the bracket notation.
+# 3 ) You can only use df.col if the column name is a valid Python identifier, be careful.
+
+# 106.16062176165804
+
+###########################################################
+# lets calculate the mean beer servings only for African countries
+# print(drinks[drinks["continent"] == "Africa"]["beer_servings"].mean())
+# 61.471698113207545
+
+###########################################################
+# lets calculate the mean of beer servings of each continent
+# print(drinks.groupby("continent")["beer_servings"].mean())
+# continent
+# Africa            61.471698
+# Asia              37.045455
+# Europe           193.777778
+# North America    145.434783
+# Oceania           89.687500
+# South America    175.083333
+# Name: beer_servings, dtype: float64
+
+# print(drinks.groupby("continent")[["beer_servings"]].mean()) #or making it dataframe
+# continent
+# Africa             61.471698
+# Asia               37.045455
+# Europe            193.777778
+# North America     145.434783
+# Oceania            89.687500
+# South America     175.083333
+###########################################################
+#  using aggregation function with group by
+# print(drinks.groupby('continent')['beer_servings'].min())
+# continent
+# Africa            0
+# Asia              0
+# Europe            0
+# North America     1
+# Oceania           0
+# South America    93
+# Name: beer_servings, dtype: int64
+
+# we can use multiple agg functions
+# print(drinks.groupby('continent')['beer_servings'].agg(['count','sum','max', 'min']))
+#                count   sum  max  min
+# continent
+# Africa            53  3258  376    0
+# Asia              44  1630  247    0
+# Europe            45  8720  361    0
+# North America     23  3345  285    1
+# Oceania           16  1435  306    0
+# South America     12  2101  333   93
+
+###########################################################
+print(drinks.groupby('continent').mean())
+#                beer_servings  ...  total_litres_of_pure_alcohol
+# continent                     ...
+# Africa             61.471698  ...                      3.007547
+# Asia               37.045455  ...                      2.170455
+# Europe            193.777778  ...                      8.617778
+# North America     145.434783  ...                      5.995652
+# Oceania            89.687500  ...                      3.381250
+# South America     175.083333  ...                      6.308333
+#
+# [6 rows x 4 columns]
+
+###########################################################
+# lets use plots
+drinks.groupby('continent').mean().plot(kind= 'bar')
+plt.show()
+###########################################################
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