[3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns: 1 2.0 NaN 2 4.0 3.0 3 NaN 4.0 4 3.0 6.0 5 7.0 8.0 In [75]: df1.combine_first(df2) Out[75]: A B 0 1.0 NaN 1 2.0 2.0 2 3.0 3.0 3 5.0 4.0 4 3.0 6.0 5 7.0 8.0 General DataFrame combine The combine_first() dft1.astype({'a': np.bool, 'c': np.float64}) In [358]: dft1 Out[358]: a b c 0 True 4 7.0 1 False 5 8.0 2 True 6 9.0 In [359]: dft1.dtypes Out[359]: a bool b int64 c float64 dtype: object Note: When trying

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In [20]: air_quality = pd.merge(air_quality, stations_coord, how="left", on="location") In [21]: [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In [20]: air_quality = pd.merge(air_quality, stations_coord, how="left", on="location") In [21]: [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In [20]: air_quality = pd.merge(air_quality, stations_coord, how="left", on="location") In [21]: [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In [20]: air_quality = pd.merge(air_quality, stations_coord, how="left", on="location") In [21]: [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 1.4. Tutorials 47 pandas: powerful Python data analysis toolkit, Release 1.2.3 In [20]: air_quality [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 1.4. Tutorials 49 pandas: powerful Python data analysis toolkit, Release 1.3.2 In [20]: air_quality [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 1.4. Tutorials 47 pandas: powerful Python data analysis toolkit, Release 1.2.0 In [20]: air_quality [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In [20]: air_quality = pd.merge(air_quality, stations_coord, ....: how='left', on='location') . [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns:

2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In this specific example, the parameter column provided by the data ensures that each of the original 2019-05-07 01:00:00+00:00 BETR801 no2 50.5 1109 2019-05-07 01:00:00+00:00 London Westminster pm25 8.0 In [20]: air_quality = pd.merge(air_quality, stations_coord, ....: how='left', on='location') . [3]: s = pd.Series([1, 3, 5, np.nan, 6, 8]) In [4]: s Out[4]: 0 1.0 1 3.0 2 5.0 3 NaN 4 6.0 5 8.0 dtype: float64 Creating a DataFrame by passing a NumPy array, with a datetime index and labeled columns: