- What is Pandas?
Pandas is a Python library used for data manipulation and analysis. - Why is Pandas used?
It helps in handling structured data easily and efficiently. - What type of data does Pandas handle?
Tabular data like Excel, CSV, SQL tables. - How to install Pandas?
Using pip: pip install pandas - How to import Pandas?
import pandas as pd - What is a Series?
A 1-dimensional labeled array in Pandas. - What is DataFrame?
A 2-dimensional table-like structure in Pandas. - Difference between Series and DataFrame?
Series = 1D, DataFrame = 2D structure. - What is Pandas used in real life?
Data cleaning, analysis, AI, ML, finance. - What is index in Pandas?
Labels used to identify rows. - What is column in Pandas?
Vertical field in DataFrame. - What is axis in Pandas?
0 = rows, 1 = columns. - What is NaN?
Represents missing values. - How to create empty DataFrame?
pd.DataFrame() - What is head() function?
Shows first 5 rows. - What is tail() function?
Shows last 5 rows. - What is shape?
Returns number of rows and columns. - What is info()?
Shows summary of DataFrame. - What is describe()?
Gives statistical summary. - What is dtype?
Shows data type of column. - How to create Series?
pd.Series([1,2,3]) - How to create DataFrame?
pd.DataFrame({‘A’:[1,2], ‘B’:[3,4]}) - Can Pandas read CSV?
Yes, using read_csv(). - Can Pandas read Excel?
Yes, using read_excel(). - How to read CSV file?
pd.read_csv(‘file.csv’) - How to write CSV?
df.to_csv(‘file.csv’) - What is dictionary in Pandas?
Used to create DataFrame. - Can Pandas use NumPy?
Yes, fully compatible. - What is list in Pandas?
Used to create Series or DataFrame. - What is ndarray?
Numpy array used in Pandas. - What is JSON support?
Pandas can read/write JSON files. - How to read JSON?
pd.read_json() - What is default index?
0,1,2,… - How to set custom index?
index=[…] - What is column selection?
Selecting specific columns from DataFrame. - How to select column?
df[‘column_name’] - How to select multiple columns?
df[[‘A’,’B’]] - What is row selection?
Selecting rows using loc/iloc. - What is loc?
Label-based indexing. - What is iloc?
Position-based indexing. - What is missing data?
Data not available (NaN). - How to check missing values?
isnull().sum() - How to remove missing values?
dropna() - How to fill missing values?
fillna() - What is duplicate data?
Repeated rows. - How to remove duplicates?
drop_duplicates() - What is sorting?
Arranging data order-wise. - How to sort values?
sort_values() - What is filtering?
Selecting specific condition data. - How to filter data?
df[df[‘A’]>10] - What is apply()?
Applies function to data. - What is lambda function?
Small anonymous function. - What is rename?
Changing column names. - How to rename column?
rename() - What is add column?
Creating new column. - How to add column?
df[‘new’]=value - How to delete column?
drop(columns=[‘A’]) - What is inplace?
Modifies original DataFrame. - What is groupby?
Group data for analysis. - What is aggregation?
Sum, mean, count operations. - What is merge?
Combines two DataFrames. - What is join?
Joins data based on index. - What is concat?
Joins DataFrames vertically/horizontally. - What is inner join?
Common records only. - What is outer join?
All records combined. - What is left join?
All left table data. - What is right join?
All right table data. - What is value_counts()?
Counts unique values. - What is unique()?
Returns unique values. - What is nunique()?
Counts unique values. - What is time series?
Date-based data analysis. - What is datetime?
Date and time format. - What is resampling?
Changing time frequency. - What is rolling?
Moving window calculations. - What is plotting?
Graph visualization. - Can Pandas plot graphs?
Yes, using plot(). - What is histogram?
Distribution graph. - What is bar chart?
Category comparison graph. - What is line chart?
Trend visualization. - Why Pandas important?
Used in AI, ML, data science.
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