Ejemplo 1: soltar nan en el marco de datos de pandas
df.dropna(subset=['name', 'born'])
Ejemplo 2: drop na pandas
>>> df.dropna(subset=['name', 'born'])
name toy born
1 Batman Batmobile 1940-04-25
Ejemplo 3: dropna pandas
>>> df.dropna(axis="columns")
name
0 Alfred
1 Batman
2 Catwoman
Ejemplo 4: umbral dropna
#dropping columns having more than 50% missing values(1994/2==1000)
df=df.dropna(thresh=1000,axis=1)
Ejemplo 5: pandas dropna
df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
"toy": [np.nan, 'Batmobile', 'Bullwhip'],
"born": [pd.NaT, pd.Timestamp("1940-04-25"),
pd.NaT]})
df
# o/p
# name toy born
# 0 Alfred NaN NaT
# 1 Batman Batmobile 1940-04-25
# 2 Catwoman Bullwhip NaT
# Drop the rows where at least one element is missing.
df.dropna()
# o/p
# name toy born
# 1 Batman Batmobile 1940-04-25
# ref. https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.dropna.html
Ejemplo 6: pandas dropna
>>> df.dropna(subset=['name', 'toy'])
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT
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