1. Which is not a rule for read cv() as related to numbers: a. If any value of a column is non-numeric, the column's values are represented as Python types as opposed to NumPy types b. If a column has integer values and some missing values, pandas will use the Python int type or None for the values in the column c. if any value in a column of numbers is missing, even is all non-missing values are integers, the column is stored as a NumPy float type d. If all values in a column are numeric and any value has decimal places, the column is represented as a NumPy float 2. For a pandas DataFrame df, which expression can be used to find rows with missing values for the column named id? df[df.id.isna()] df.id.isna df.missing('id') df.id. False
1. Which is not a rule for read cv() as related to numbers:
a. If any value of a column is non-numeric, the column's values are represented as Python types as opposed to NumPy types
b. If a column has integer values and some missing values, pandas will use the Python int type or None for the values in the column
c. if any value in a column of numbers is missing, even is all non-missing values are integers, the column is stored as a NumPy float type
d. If all values in a column are numeric and any value has decimal places, the column is represented as a NumPy float
2. For a pandas DataFrame df, which expression can be used to find rows with missing values for the column named id?
df[df.id.isna()]
df.id.isna
df.missing('id')
df.id. False
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