Page 89 - Informatics_Practices_Fliipbook_Class12
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Female;65-69;19773
Male;50-54;36249
Male;70-74;12639
Male;85-89;1718
Female;40-44;44528
Female;75-79;8407
Female;90-94;812
Male;5-9;63226
Male;20-24;66257
Male;45-49;42078
Male;60-64;25049
Male;0-4;59967
Male;10-14;65685
Male;15-19;67336
Male;35-39;54325
Male;40-44;47639
Male;65-69;19370
Male;90-94;568
Male;100+;15
Female;5-9;57728
Female;20-24;59916
Female;25-29;57608
Female;70-74;13496
Female;80-84;5115
Female;85-89;2401
3. Consider the following dictionary comprising students' details:
data = {'Name': ['Hetansh', 'Supriya', 'Mehak', 'Madhu', 'Rama'],
'RollNumber': [10, 11, 12, 13, 14],
'Grade': ['A', 'B', 'A', 'B', 'C'],
'Marks': [99, 82, 95, 80, 60]
}
Create a Pandas DataFrame studentDF using the above dictionary.
4. Consider the following Pandas DataFrame df:
Name RollNumber Grade Marks
0 Hetansh 10 A 99
1 Supriya 11 B 82
2 Mehak 12 A 95
3 Madhu 13 B 80
4 Rama 14 C 60
Determine the output of the following statements:
(i) print(df.ndim)
(ii) print(df.shape)
(iii) print(df.index)
(iv) print(df.columns)
(v) print(df.head(5))
(vi) print(df.tail(2))
(vii) print(df.iloc[2])
(viii) print(df.loc[1,'Name'])
(ix) print(df.set_index('RollNumber'))
(x) print(df[df['Marks'] > 98])
Data Handling using Pandas DataFrame 75

