>>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob True 0 >>> df2_transposed = df2.T # or df2.transpose() >>> df2_transposed 0 1 name Alice Bob score 9.5 8 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame reference pandas: powerful Python data analysis toolkit, Release 1.0.0 >>> df2.dtypes name object score float64 employed bool kids int64 dtype: object >>> df2_transposed.dtypes 0 object 1 object dtype:

>>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob name Alice Bob score 9.5 8 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype: >>> df2.dtypes name object score float64 employed >>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob

>>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob name Alice Bob score 9.5 8 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype: >>> df2.dtypes name object score float64 employed >>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob

>>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob True 0 >>> df2_transposed = df2.T # or df2.transpose() >>> df2_transposed 0 1 name Alice Bob score 9.5 8 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame reference pandas: powerful Python data analysis toolkit, Release 1.0.5 >>> df2.dtypes name object score float64 employed bool kids int64 dtype: object >>> df2_transposed.dtypes 0 object 1 object dtype:

>>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob True 0 >>> df2_transposed = df2.T # or df2.transpose() >>> df2_transposed 0 1 name Alice Bob score 9.5 8 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame DataFrame 1365 pandas: powerful Python data analysis toolkit, Release 1.0.4 >>> df2.dtypes name object score float64 employed bool kids int64 dtype: object >>> df2_transposed.dtypes 0 object 1 object dtype:

>>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob name Alice Bob score 9.5 8 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype: >>> df2.dtypes name object score float64 employed >>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob

>>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob True 0 >>> df2_transposed = df2.T # or df2.transpose() >>> df2_transposed 0 1 name Alice Bob score 9.5 8 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame DataFrame 1367 pandas: powerful Python data analysis toolkit, Release 1.0.3 >>> df2.dtypes name object score float64 employed bool kids int64 dtype: object >>> df2_transposed.dtypes 0 object 1 object dtype:

specificity here but for our purposes it suffices to summarize the key points: A CSS importance score for each HTML element is derived by starting at zero and adding: • 1000 for an inline style attribute >>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob name Alice Bob score 9.5 8.0 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype: >>> df2.dtypes name object score float64 employed

specificity here but for our purposes it suffices to summarize the key points: A CSS importance score for each HTML element is derived by starting at zero and adding: • 1000 for an inline style attribute >>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob name Alice Bob score 9.5 8.0 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype: >>> df2.dtypes name object score float64 employed

specificity here but for our purposes it suffices to summarize the key points: A CSS importance score for each HTML element is derived by starting at zero and adding: • 1000 for an inline style attribute >>> d2 = {'name': ['Alice', 'Bob'], ... 'score': [9.5, 8], ... 'employed': [False, True], ... 'kids': [0, 0]} >>> df2 = pd.DataFrame(data=d2) >>> df2 name score employed kids 0 Alice 9.5 False 0 1 Bob name Alice Bob score 9.5 8.0 employed False True kids 0 0 When the DataFrame has mixed dtypes, we get a transposed DataFrame with the object dtype: >>> df2.dtypes name object score float64 employed