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Analyzing Data from Pandas

data = { "Duration":{ "0":60, "1":60, "2":60, "3":45, "4":45, "5":60 }, "Pulse":{ "0":110, "1":117, "2":103, "3":109, "4":117, "5":102 }, "Maxpulse":{ "0":…

Bash
data = {
  "Duration":{
    "0":60,
    "1":60,
    "2":60,
    "3":45,
    "4":45,
    "5":60
  },
  "Pulse":{
    "0":110,
    "1":117,
    "2":103,
    "3":109,
    "4":117,
    "5":102
  },
  "Maxpulse":{
    "0":130,
    "1":145,
    "2":135,
    "3":175,
    "4":148,
    "5":127
  },
  "Calories":{
    "0":409,
    "1":479,
    "2":340,
    "3":282,
    "4":406,
    "5":300
  }
}

df = pd.DataFrame(data)
print(df) 

head/tail:

print(df.info()):

SQL
# dropna(): method returns a new DataFrame by default, and will not change the original.
# dropna(inplace = True) will NOT return a new DataFrame, but it will remove all rows containing NULL values from the original DataFrame.
print(df.dropna(inplace=False))

Pandas - DataFrame Reference

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