drop na pandy
>>> df.dropna(subset=['name', 'born'])
name toy born
1 Batman Batmobile 1940-04-25
pokles na pandy
>>> df.dropna(subset=['name', 'born'])
name toy born
1 Batman Batmobile 1940-04-25
vrácení nan v datovém pandy
df.dropna(subset=['name', 'born'])
pandy dropna
df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
... "toy": [np.nan, 'Batmobile', 'Bullwhip'],
... "born": [pd.NaT, pd.Timestamp("1940-04-25"),
... pd.NaT]})
>>> df
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()
name toy born
1 Batman Batmobile 1940-04-25
dropna pandy
>>> df.dropna(axis='columns')
name
0 Alfred
1 Batman
2 Catwoman
drop null řádky pandy
df.dropna()
dropna pandy
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
dropna práh
#dropping columns having more than 50% missing values(1994/2==1000)
df=df.dropna(thresh=1000,axis=1)
odstranit nans v df python
df[~np.isnan(df)]
smazat nans v DF Pythonu
df[~np.isnan(df)]
prahová hodnota u pand dropna
>>>df = pd.DataFrame({"name": ['Alfred', 'Batman', 'Catwoman'],
"toy": [np.nan, 'Batmobile', 'Bullwhip'],
"born": [pd.NaT, pd.Timestamp("1940-04-25"),
pd.NaT]})
df.dropna(thresh=2)
name toy born
1 Batman Batmobile 1940-04-25
2 Catwoman Bullwhip NaT