Missing Values
~11 mincode completion
Write fill_missing_with_mean(df, column) that replaces the missing values in one column with that column's mean, and returns the DataFrame.
Examples
The gap is filled with the mean of the values that were present
- Input
- fill_missing_with_mean({"x": [1, None, 3]}, "x")
- Output
- [1, 2, 3]
A column with no gaps is left exactly as it was
- Input
- fill_missing_with_mean({"x": [4, 6]}, "x")
- Output
- [4, 6]
No rows are dropped, the row count is unchanged
- Input
- fill_missing_with_mean({"x": [1, None, None, 5]}, "x")
- Output
- 4
Hints
Hint 1
A list comprehension expresses this in one line.
Hint 2
A common slip here: dropped rows instead of filling.
Requirements
: a DataFrame
column: the name of the column to fillReturn the DataFrame with that column's gaps filled.
Constraints
Standard library only, no imports required
Time limit: 200 ms, Memory: 64 MB
Where this shows up
~11 min
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Python
import pandas as pd
def fill_missing_with_mean(df, column):
"""
Fill NaNs in one column with that column's mean.
Args:
df: a DataFrame
column: the name of the column to fill
Returns:
The DataFrame with that column's gaps filled.
"""
# YOUR CODE HERE
pass
def _values(df, column):
import pandas as pd
return [float(v) for v in fill_missing_with_mean(pd.DataFrame(df), column)[column]]
def _row_count(df, column):
import pandas as pd
return int(len(fill_missing_with_mean(pd.DataFrame(df), column)))