Pandas Dataframe creation
+ +import pandas as pd
+import numpy as np
+
+table = pd.DataFrame()
+a = ['arunn', 'nandhu','aravind','balaji','karuna','sagar']
+b= [70,55,55,50,50,50]
+c = [170,160,160,170,160,150]
+table['name'] = a
+table['weight'] = b
+table['height'] = c
+table
+PYTHON PANDAS SYNTAX
+ +# SELECT *
+
+table;
+
+
+
+
+# SELECT n
+
+table[['name','height']];
+
+
+
+# COUNT
+
+len(table);
+
+
+
+# UNIQUE VALUES
+
+table['name'].unique();
+
+
+
+
+# WHERE CONDITION -- =,<,>,!=
+
+table[table['name'] == 'arunn'];
+
+
+#AND/OR
+
+table[(table['height'] == 150) & (table['weight'] == 70)];
+
+table[(table['height'] == 150) | (table['weight'] == 70)];
+
+
+
+#IN
+
+table[table['height'].isin([150,170])];
+
+
+
+#NULL/NOTNULL
+
+table[table['height'].isna()];
+
+table[table['height'].notna()];
+
+
+
+#LIKE/NOTLIKE
+
+table[table['name'].str.startswith('ar')];
+
+table[table['name'].str.endswith('ar')];
+
+table[table['name'].str.contains('nn')];
+
+
+
+#AGG --SUM,MIN,MAX,AVG
+
+table[['height','weight']].apply(np.sum);
+
+table.agg(mean, axis = "columns");
+
+table.agg({ 'height' : [min] , 'weight' : [min , max] });
+
+
+
+#ALIASING --AS
+
+table.rename(index = str , columns = {'name' : 'NAME' , 'height' :'HEIGHT' , 'weight' : 'WEIGHT'});
+
+
+#SORT
+
+table.sort_values('name');
+
+table.sort_values('name', ascending = False);
+
+table.sort_values(['name','height']);
+
+
+
+
+#GROUP -- WITH AGG
+
+table['weight'].groupby(table['height']).sum()
+
+
+#HAVING
+
+table['weight'].groupby(table['height']).filter(lambda x : x.sum() == 120);
+
+
+#UPDATE
+
+table.loc[table['weight'] == 50, 'height'] = table.loc[table['weight'] == 50,'height'] + 10;
+
+
+#DELETE
+
+table.loc[table['weight'] == 50, 'height'] = np.NaN
+
+table = table.dropna()
+
+#Applying function to a pandas columns:
+
+def funct(val):
+ if val['weight'] == 70:
+ return 700
+ elif val['weight'] == 50:
+ return 500
+ else:
+ return 550
+
+
+table['weight'] = table.apply(funct, axis = 1)
+table
+