Скачать или смотреть #29 Pandas: Missing values - 2 - 15 | Tutorial

The video discusses how to handle missing values in Python.

Timeline & Data
(Python 3.7)

00:00 - Welcome
00:08 - Outline of video
00:53 - Open Jupyter notebook
01:04 - Data
02:17 - .isna(), .notna()
04:29 - NaN==NaN; None==None
06:16 - Missing values in date time: NaT
07:20 - Important !!! note about: .iloc() and .loc()
09:15 - Insert missing values: Numeric
11:00 - Insert missing values: Object
12:31 - Calculations: +
13:37 - Calculations: .mean()
14:48 - Calculations: cumsum()
16:07 - ———————— Correction !!! Here I mean “… 1 plus 2 is 3 …”
16:54 - Calculations: .sum()
17:17 - Calculations: .prod()
17:30 - Groupby
18:39 - Ending notes

############
* * * Data * * *
############
dfa = pd.DataFrame({
'x': np.random.randint(low=1, high=5, size=5),
'y': np.random.randint(low=0, high=5, size=5)
})
dfa.loc[0:2,'x']=np.nan
dfb = dfa.copy()
dfb['z']=dfb['y']

df = pd.DataFrame({
'x': np.random.randint(0,5, 5),
'y': [np.nan,'B',None,'A','C'],
'z': [True, False, False, np.nan, True]
},
index=pd.Series([11,13,15,17,20])
)
df2 = df.reindex([11,12,13,14,15,17,18,19,20])
############