Correct Answer : Option (A) : Panel Data
Explanation : pandas is a software library written for the Python programming language for data manipulation and analysis. In particular, it offers data structures and operations for manipulating numerical tables and time series. The name is derived from the term "Panel Data", an econometrics term for data sets that include observations over multiple time periods for the same individuals. Its name is a play on the phrase "Python data analysis" itself.
Correct Answer : Option (C) : Wes McKinney
Explanation : Pandas is a Python library for data analysis. Started by Wes McKinney in 2008 out of a need for a powerful and flexible quantitative analysis tool, pandas has grown into one of the most popular Python libraries.
Correct Answer : Option (D) : pandas.Series( data, index, dtype, copy)
Correct Answer : Option (D) : pandas.DataFrame( data, index, columns, dtype, copy)
Correct Answer : Option (B) : Python
Correct Answer : Option (C) : Both A and B
Correct Answer : Option (A) : DataFrame
Correct Answer : Option (D) : All of the above
Correct Answer : Option (B) : DataFrame.from_items
Correct Answer : Option (C) : freedapi
Correct Answer : Option (A) : Pandas
Correct Answer : Option (B) : Matplotlib
Correct Answer : Option (D) : 0
Correct Answer : Option (C) : Both of the above
import pandas as pd s = pd.Series([1,2,3,4,5],index = [‘a’,’b’,’c’,’d’,’e’]) print(s[‘a’])​
Correct Answer : Option (A) : 1
import pandas as pd import numpy as np s = pd.Series(np.random.randn(2)) print(s.size)
Correct Answer : Option (B) : 2
Correct Answer : Option (D) : Returns the number of dimensions of the underlying data, by definition 1.
Correct Answer : Option (A) : Seaborn
Explanation : Seaborn has great support for pandas data objects.
Explanation : The passed index is a list of axis labels.
Correct Answer : Option (B) : major_axis
Explanation : major_axis : axis 1, it is the index (rows) of each of the DataFrames.
Correct Answer : Option (A) : Series
Correct Answer : Option (C) : Index
Correct Answer : Option (B) : Statsmodels
Explanation : Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies.
Correct Answer : Option (D) : read_csv
Correct Answer : Option (B) : DataFrame
Explanation : You get columns out of a DataFrame the same way you get elements out of a dictionary.
Correct Answer : Option (A) : yhat
Correct Answer : Option (C) : Quandl
Correct Answer : Option (C) : apply function
Correct Answer : Option (B) : SparseArray
Explanation : SparseArray is a 1-dimensional ndarray-like object storing only values distinct from the fill_value.
Correct Answer : Option (D) : print(exp.axes)
Correct Answer : Option (A) : df = pd.DataFrame(dict1)
Correct Answer : Option (A) : Blaze
Explanation : If your work entails maps and geographical coordinates, and you love pandas, you should take a close look at Geopandas.
Correct Answer : Option (D) : None of the above
Explanation : SparseArray can be converted back to a regular ndarray by calling to_dense.
Correct Answer : Option (C) : to_text
Correct Answer : Option (B) : SparseList
Correct Answer : Option (C) : SparseSeries.to_coo()
Explanation : Experimental api to transform between sparse pandas and scipy.sparse structures.
Correct Answer : Option (A) : Union
Correct Answer : Option (D) : transpose
Correct Answer : Option (B) : exp.iloc[0:3,0:3]
Correct Answer : Option (B) : Row
Correct Answer : Option (C) : Both are true
Correct Answer : Option (A) : len(df)
import pandas as pd import numpy as np s = pd.Series(np.random.randn(4)) print s.ndim
Correct Answer : Option (A) : Returns the number of dimensions of the object. By definition, a Series is a 1D data structure, so it returns 1.
import pandas as pd s = pd.Series([1,2,3,4,5],index = ['a','b','c','d','e']) print s['a']
Correct Answer : Option (D) : 1
import pandas as pd import numpy as np s = pd.Series(np.random.randn(2)) print s.size
Correct Answer : Option (C) : 2
Explaination : Returns the size(length) of the series.
import pandas as pd S1 = pd.Series(data = (31, 2, -6), index = [7, 9, 3, 2]) print(S1)
Correct Answer : Option (A) : ValueError
df = pd.DataFrame({‘c1’:[12,34,45],’c2’:[32,21,44],’c3’:[74,41,20]}) print(df.index)
Correct Answer : Option (B) : RangeIndex(start=0, stop=3, step=1)