Path: blob/master/06_Stats/US_Baby_Names/Solutions.ipynb
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Kernel: Python 3
US - Baby Names
Introduction:
We are going to use a subset of US Baby Names from Kaggle. In the file it will be names from 2004 until 2014
Step 1. Import the necessary libraries
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Step 2. Import the dataset from this address.
Step 3. Assign it to a variable called baby_names.
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Out[2]:
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1016395 entries, 0 to 1016394
Data columns (total 7 columns):
# Column Non-Null Count Dtype
--- ------ -------------- -----
0 Unnamed: 0 1016395 non-null int64
1 Id 1016395 non-null int64
2 Name 1016395 non-null object
3 Year 1016395 non-null int64
4 Gender 1016395 non-null object
5 State 1016395 non-null object
6 Count 1016395 non-null int64
dtypes: int64(4), object(3)
memory usage: 54.3+ MB
Step 4. See the first 10 entries
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Out[3]:
Step 5. Delete the column 'Unnamed: 0' and 'Id'
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Out[4]:
Step 6. What year has the highest number of baby names in the dataset?
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Out[5]:
Count 2007
dtype: int64
Step 7. Are there more male or female names in the dataset?
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Out[5]:
F 558846
M 457549
Name: Gender, dtype: int64
Step 8. Group the dataset by name and assign to names
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Out[6]:
(17632, 1)
Step 9. How many different names exist in the dataset?
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Out[7]:
17632
Step 10. What is the name with most occurrences?
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Out[8]:
'Jacob'
Step 11. How many different names have the least occurrences?
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Out[9]:
2578
Step 12. What is the median name occurrence?
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Out[10]:
Step 13. What is the standard deviation of names?
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Out[11]:
11006.069467891111
Step 14. Get a summary with the mean, min, max, std and quartiles.
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Out[12]: