Pandas Basics

Question 1

Import library

pandas

Answer 1

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

Question 2

Import a csv into data frame

pandas

Answer 2

file = "file.csv"
df = pd.read_csv(file)

Question 3

Export a data frame to csv

pandas

Answer 3

df.to_csv("file.csv", sep = "|", index = F

Question 4

Creating a data frame from a list of lists

pandas

Answer 4

data = [[1, 2, "A"], [3, 4, "B"]]
df = pd.DataFrame(data, 
           columns = ["col1", "col2", "col3"])

Question 5

Creating a data frame from a dictionary

pandas

Answer 5

data = {'col1': [1, 2], 
        'col2': [3, 4], 
        'col3': ["A", "B"]}

df = pd.DataFrame(data=data)

Question 6

Get number of rows and columns in a data frame

pandas

Answer 6

df.shape

Question 7

Viewing top n rows

pandas

Answer 7

df.head(n)

Question 8

Displaying data type of columns

pandas

Answer 8

df.dtypes

Question 9

Modifying the data type of a column

pandas

Answer 9

df["col1"] = df["col1"].astype(np.int8)

Question 10

Display missing value stats and data type

pandas

Answer 10

df.info()

Question 11

Print descriptive stats

pandas

Answer 11

df.describe()

Question 12

Filling missing values with a specific value

pandas

Answer 12

df.fillna(0, inplace = True)

Question 13

Combining data frames: join (merge)

pandas

Answer 13

pd.merge(df1, df2, on = "col3")

Question 14

Sorting a data frame

pandas

2 alternatives

Answer 14

df.sort_values("col1"))
df.sort_values(by='Sales', ascending=False)

Question 15

Grouping a data frame

pandas

2 alternatives

Answer 15

df.groupby('Region')['Sales'].mean()
df.groupby("col3").agg({"col1":sum, "col2":max})

Question 16

Renaming columns

pandas

Answer 16

df.rename(columns = {"col_A":"col1"})

Question 17

Deleting columns

pandas

Answer 17

df.drop(columns = ["col1"])

Question 18

Adding columns (addition method)

pandas

Answer 18

df["col3"] = df["col1"] + df["col2"]

Question 19

Adding columns (assingment method)

pandas

Answer 19

df = df.assign(col3 = df["col1"] + df["col2"])

Question 20

Filtering rows: boolean method

pandas

Answer 20

dfx[['b', 'c']]
df[df["col2"] > 5]
df[(df['Region'] == 'North') & (df['Sales'] > 100)]

Question 21

Filtering rows: from list

pandas

Answer 21

filter_list = ["A", "C"]
df[df["col3"].isin(filter_list)]

Question 22

Filtering by position

pandas

Answer 22

dfx.iloc[1] #Select single row
dfx.iloc[:,1] #Select single column
dfx.iloc[1,1] #Select single cell
dfx.iloc[:2,:2] #Select group of cells
dfx.iloc[1:,1:] #Select group of cells

Question 23

Filtering: selecting by index

pandas

Answer 23

dfx.loc[1] #Select single row
dfx.loc[:,'b'] #Select single column
dfx.loc[1,'b'] #Select single cell
dfx.loc[:2,['b', 'c']]  #Select group of cells



dfx.loc['hola'] #Select single row
dfx.loc[:,'c'] #Select single column
dfx.loc['hola','b'] #Select single cell
dfx.loc[:'hola',['b', 'c']]  #Select group of cells


data.loc[data['condition']]

Question 24

Set/reset index

pandas

Answer 24

dfx.set_index('d', inplace=True)
dfx.reset_index()

Question 25

Finding unique values (list, count)

pandas

Answer 25

df["col3"].unique()
df["col3"].nunique()

Question 26

Apply a function to a data frame

pandas

Answer 26

def add_cols(row):
    return row.col1 + row.col2
df["col3"] = df.apply(add_cols, axis=1)

Question 27

Apply a function to a single column

pandas

Answer 27

def square_col(num):
    return num**2
df["col3"] = df.col1.apply(square_col)
OR
data['new_column'] = data['old_column'].apply(lambda x: x * 2)

Question 28

Mark duplicated rows

pandas

Answer 28

df.duplicated(keep=False)

Question 29

Drop duplicated rows

pandas

Answer 29

df.drop_duplicates()

Question 30

Frequency distribution

pandas

Answer 30

df.value_counts("col2")

Question 31

Reset the index, drop the old index

Answer 31

print(df.reset_index())
df.reset_index(drop=True)

Question 32

Crosstbulation

pandas

Answer 32

pd.crosstab(df.col1, df.col2)

Question 33

Pivoting a dataset (to wide format)

pandas

Answer 33

pd.pivot_table(df, 
               index = ["Name"],
               columns=["Subject"], 
               values='Marks',
               fill_value=0)

Question 34

Get the type of an object

pandas

Answer 34

type(df)

Question 35

Drop rows with missing values

pandas

Answer 35

df.dropna()

Question 36

Apply a lambda function

pandas

Answer 36

df['Sales'].apply(lambda x: x * 2)

Question 37

Combining data frames: append

pandas

Answer 37

df2 = pd.concat([df, df])

Question 38

Get number of row and columns

Answer 38

df.shape

Question 39

Delete a dataframe

Answer 39

del df
del(df)

Question 40

Add a caption to a dataframe

Answer 40

caption = ‘This is a caption’

df.style.set_caption(caption)

Question 41

Import from Excel

Answer 41

From Excel

data = pd.read_excel(‘data.xlsx’)

Question 42

Import from SQL

Answer 42

import sqlite3
conn = sqlite3.connect(‘database.db’)
data = pd.read_sql_query(‘SELECT * FROM table_name’, conn)

Question 43

Drop rows with missing values

Answer 43

data.dropna()

Question 44

Trim outliers

Answer 44

Q1 = data[‘column’].quantile(0.25)
Q3 = data[‘column’].quantile(0.75)
IQR = Q3 - Q1
data = data[(data[‘column’] >= Q1 - 1.5 * IQR) & (data[‘column’] <= Q3 + 1.5 * IQR)]

Question 45

Save data to csv

Answer 45

data.to_csv(‘processed_data.csv’, index=False)

Question 46

Manipulate dates

Answer 46

data[‘date_column’] = pd.to_datetime(data[‘date_column’])
data[‘month’] = data[‘date_column’].dt.month

Question 47

Merging dataframes

Answer 47

merged_data = pd.concat([data1, data2], axis=0)

Question 48

Pivot table

Answer 48

pd.pivot_table(data, values=’value’, index=’category’, columns=’date’, aggfunc=np.sum)

Question 49

Random sample of data

Answer 49

sample = data.sample(n=100)

Question 50

Merging data frame based on common column

Answer 50

merged_df = pd.merge(df1, df2, on=’ID’)

Question 51

Joining based on index

Answer 51

result = df1.join(df2)