Pandas is an effective data analysis tool in Python. These 12 interview questions on Pandas helpful to practice and crack the interview.
Pandas Interview Questions
#1What is Pandas?
Pandas is an open-source Python library that is used for data manipulation and analysis. It provides a variety of functions and tools for working with structured data, such as data frames and series.
#2 What is a data frame in Pandas?
A data frame is a two-dimensional table-like data structure in Pandas. It is similar to a spreadsheet or a SQL table and consists of rows and columns. Each column can contain data of a different data type.
#3 What is a Series in Pandas?
A Series is a one-dimensional labeled array in Pandas. It can hold data of any type, including numeric, character, and boolean data. Each element of the series has a label, which can be used to access the data.
#4 How can you read a CSV file using Pandas?
You can use the read_csv() function in Pandas to read a CSV file. For example:
import pandas as pd
df = pd.read_csv('filename.csv')#5 How can you select a subset of columns in a Pandas data frame?
You can use the bracket notation to select a subset of columns in a Pandas data frame. For example:
import pandas as pd
df = pd.read_csv('filename.csv')
subset = df[['column1', 'column2']]#6 How can you filter rows in a Pandas data frame based on a condition?
You can use boolean indexing to filter rows in a Pandas data frame based on a condition. For example:
import pandas as pd
df = pd.read_csv('filename.csv')
subset = df[df['column'] > 10]#7 How can you merge two data frames in Pandas?
You can use the merge() function in Pandas to merge two data frames based on a common column. For example:
import pandas as pd
df1 = pd.read_csv('filename1.csv')
df2 = pd.read_csv('filename2.csv')
merged = pd.merge(df1, df2, on='column')#8 How can you group data in a Pandas data frame based on a column?
You can use the groupby() function in Pandas to group data in a data frame based on a column. For example:
import pandas as pd
df = pd.read_csv('filename.csv')
grouped = df.groupby('column')#9 How can you pivot a Pandas data frame?
You can use the pivot() function in Pandas to pivot a data frame.
For example:
import pandas as pd
df = pd.read_csv('filename.csv')
pivoted = df.pivot(index='column1', columns='column2', values='value')#10 How can you handle missing values in a Pandas data frame?
You can use the fillna() function in Pandas to replace missing values in a data frame with a specified value. For example:
import pandas as pd
df = pd.read_csv('filename.csv')
df.fillna(0, inplace=True)#11 How can you plot data in a Pandas data frame?
You can use the plot() function in Pandas to plot data in a data frame. For example:
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv('filename.csv')
df.plot(x='column1', y='column2', kind='line')
plt.show()#12 How can you export a Pandas data frame to a CSV file?
You can use the to_csv() function in
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