Knowee
Questions
Features
Study Tools

import pandas as pd  a = {'col1': [1, 2], 'col2': [3, 4]}  info = pd.DataFrame(data=a)  info.dtypes  # We convert it into 'int64' type.  info.astype('int64').dtypes  info.astype({'col1': 'int64'}).dtypes  x = pd.Series([1, 2], dtype='int64')  x.astype('category')  cat_dtype = pd.api.types.CategoricalDtype(  categories=[2, 1], ordered=True)  x.astype(cat_dtype)  x1 = pd.Series([1,2])  x2 = x1.astype('int64', copy=False)  x2[0] = 10  x1  # note that x1[0] has changed too

Question

import pandas as pd  a = {'col1': [1, 2], 'col2': [3, 4]}  info = pd.DataFrame(data=a)  info.dtypes  # We convert it into 'int64' type.  info.astype('int64').dtypes  info.astype({'col1': 'int64'}).dtypes  x = pd.Series([1, 2], dtype='int64')  x.astype('category')  cat_dtype = pd.api.types.CategoricalDtype(  categories=[2, 1], ordered=True)  x.astype(cat_dtype)  x1 = pd.Series([1,2])  x2 = x1.astype('int64', copy=False)  x2[0] = 10  x1  # note that x1[0] has changed too

...expand
🧐 Not the exact question you are looking for?Go ask a question

Solution

The text you provided is a Python script. Here's a step-by-step explanation of what it does:

  1. It imports the pandas library, which is a powerful data manipulation library in Python.

  2. It creates a dictionary a with two keys, 'col1' and 'col2', each associated with a list of two integers.

  3. It converts this dictionary into a pandas DataFrame info. A DataFrame is a two-dimensional labeled data structure with columns of potentially different types.

  4. It checks the data types of the columns in the DataFrame using info.dtypes.

  5. It converts the data types of all columns in the DataFrame to 'int64' using info.astype('int64').dtypes.

  6. It converts the data type of 'col1' in the DataFrame to 'int64' using info.astype({'col1': 'int64'}).dtypes.

  7. It creates a pandas Series x with two integers and data type 'int64'. A Series is a one-dimensional labeled array capable of holding any data type.

  8. It converts the data type of the Series to 'category' using x.astype('category').

  9. It creates a categorical data type cat_dtype with categories [2, 1] and ordered=True, and converts the Series to this data type using x.astype(cat_dtype).

  10. It creates another pandas Series x1 with two integers.

  11. It converts the data type of x1 to 'int64' without making a copy using x1.astype('int64', copy=False), and assigns the result to x2.

  12. It changes the first element of x2 to 10 using x2[0] = 10.

  13. It prints x1 and notes that the first element of x1 has changed too, because x2 is not a copy of x1, but a view on the same data.

This problem has been solved

Similar Questions

import pandas as pd  a = {'col1': [1, 2], 'col2': [3, 4]}  info = pd.DataFrame(data=a)  info.dtypes  # We convert it into 'int64' type.  info.astype('int64').dtypes  info.astype({'col1': 'int64'}).dtypes  x = pd.Series([1, 2], dtype='int64')  x.astype('category')  cat_dtype = pd.api.types.CategoricalDtype(  categories=[2, 1], ordered=True)  x.astype(cat_dtype)  x1 = pd.Series([1,2])  x2 = x1.astype('int64', copy=False)  x2[0] = 10  x1  # note that x1[0] has changed too

import pandas as pd  import numpy as np    info = pd.DataFrame(np.random.randn(4,2),columns = ['col1','col2'])  for row_index,row in info.iterrows():     print (row_index,row)  Output0 name John degree B.Techscore 90Name: 0, dtype: object1 name Smithdegree B.Comscore 40Name: 1, dtype: object2 name Alexanderdegree M.Comscore 80Name: 2, dtype: object3 name Williamdegree M.Techscore 98Name: 3, dtype: object

If df is a Dataframe with 3 columns Col1, Col2 and Col3 , what type of data is df[‘Col1’]?One dimensional numpy arrayOne dimensional ListSeriesDataframe with one column

import pandas as pd   info = pd.DataFrame({'A': {0: 'p', 1: 'q', 2: 'r'},  'B': {0: 40, 1: 55, 2: 25},  'C': {0: 56, 1: 62, 2: 42}})  pd.melt(info, id_vars=['A'], value_vars=['C'])  pd.melt(info, id_vars=['A'], value_vars=['B', 'C'])  pd.melt(info, id_vars=['A'], value_vars=['C'],  var_name='myVarname', value_name='myValname')

Which is correct way to define featuresX = df[['clo1', 'col2', . . .]]X = df[clo1, col2, . . .]X = df['clo1', 'col2', . . .]X = df[[clo1, col2, . . .]]

1/1

Upgrade your grade with Knowee

Get personalized homework help. Review tough concepts in more detail, or go deeper into your topic by exploring other relevant questions.