To get a link to the csv
file you are using, click here .
Loading libraries:
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Loading data:
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Output:
Type: class ’pandas.core.frame.DataFrame’ Head - State Population Murder.Rate Abbreviation 0 Alabama 4779736 5.7 AL 1 Alaska 710231 5.6 AK 2 Arizona 6392017 4.7 AZ 3 Arkansas 2915918 5.6 AR 4 California 37253956 4.4 CA 5 Colorado 5029196 2.8 CO 6 Connecticut 3574097 2.4 CT 7 Delaware 897934 5.8 DE 8 Florida 18801310 5.8 FL 9 Georgia 9687653 5.7 GA Tail - State Population Murder.Rate Abbreviation 40 South Dakota 814180 2.3 SD 41 Tennessee 6346105 5.7 TN 42 Texas 25145561 4.4 TX 43 Utah 2763885 2.3 UT 44 Vermont 625741 1.6 VT 45 Virginia 8001024 4.1 VA 46 Washington 6724540 2.5 WA 47 West Virginia 1852994 4. 0 WV 48 Wisconsin 5686986 2.9 WI 49 Wyoming 563626 2.7 WY
Code # 1: Add Column to Data Frame
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Output:
State Population Murder.Rate Abbreviation PopulationInMillions 0 Alabama 4779736 5.7 AL 4.779736 1 Alaska 710231 5.6 AK 0.710231 2 Arizona 6392017 4.7 AZ 6.392017 3 Arkansas 2915918 5.6 AR 2.915918 4 California 37253956 4.4 CA 37.253956Code
: Data Description
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Output:

Code # 3: Data Information
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Output:
RangeIndex: 50 entries, 0 to 49 Data columns (tota l 4 columns): State 50 non-null object Population 50 non-null int64 Murder.Rate 50 non-null float64 Abbreviation 50 non-null object dtypes: float64 (1), int64 (1), object (2) memory usage: 1.6+ KB
Code # 4: renaming a column header
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Exit:
[’State’,’ Population’, ’MurderRate’,’ Abbreviation’]
Code # 5: Average calculation
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Exit:
Population Mean: 6162876.3 MurderRate Mean: 4.066
Code # 6: Truncated Average
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Output:
Population trimmed mean: 4783697.125 Murde rRate trimmed mean: 3.9450000000000003
Code # 7: weighted average
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Output:
Weighted MurderRate Mean: 4.445833981123393
Code # 8: Median
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Output:
Population median: 4436369.5 MurderRate median: 4.0