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Author: Algorithmica

2019-october/1.python/5.data frames1.py

@@ -24,12 +24,17 @@
 print(df1.shape)
 print(df1.info())
 
+#retrieve any column
+df1.age
+df1['age']
+
 print(df1.index)
 print(df1.values)
 df1 = df1.set_index(df1.age)
 df1 = df1.reset_index(drop=True)
 
-
 titanic_train = pd.read_csv('C:/Users/Algorithmica/Downloads/titanic_train.csv')
+print(type(titanic_train))
+print(id(titanic_train))
 print(titanic_train.shape)
-print(titanic_train.info())
+print(titanic_train.info())

2019-october/1.python/6.dataframes2.py

@@ -0,0 +1,42 @@
+import pandas as pd
+import os
+
+dir = 'E:/'
+titanic_train = pd.read_csv(os.path.join(dir, 'train.csv'))
+
+#explore the structure of dataframe
+print(titanic_train.shape)
+print(titanic_train.columns)
+print(titanic_train.dtypes)
+print(titanic_train.index)
+print(titanic_train.values)
+print(titanic_train.info())
+
+#explore sample data
+print(titanic_train.head(4))
+print(titanic_train.tail(4))
+print(titanic_train.sample(n=4))
+print(titanic_train.sample(frac=0.1))
+
+#row access with slicing operator or boolean indexing
+print(titanic_train[0:3])
+print(titanic_train[titanic_train.Sex=='male'])
+
+#column access with single value or list of values
+print(titanic_train[ ['Name', 'Age', 'Sex'] ])
+print(titanic_train['Name']) #dictionary style access
+print(titanic_train.Name) #property style access
+
+#row and column access based on index
+titanic_train.iloc[1:3,:]
+titanic_train.iloc[1:3,2:4]
+titanic_train.iloc[1:3, [True,True]]
+titanic_train.iloc[[True,True], [True, False, True]]
+#row and column access based on name
+titanic_train.loc[1:3, ['Sex','Fare']]
+titanic_train.loc[titanic_train.Sex=='male',:]
+titanic_train.loc[1:3, :'Fare']
+
+#creating new columns
+titanic_train['dummy'] = 1
+titanic_train['FamilySize'] = titanic_train['Parch'] + titanic_train['SibSp'] + 1