Pandas Basics

 

1. Pandas Series

1.1. Series

import pandas as pd

a = [1, 7, 2]

myvar = pd.Series(a)

print(myvar)

1.2. with index

a = [1, 7, 2]

myvar = pd.Series(a, index = ["x", "y", "z"])

print(myvar)

1.3. set of (keys, values)

calories = {"day1": 420, "day2": 380, "day3": 390}

myvar = pd.Series(calories)

print(myvar)

calories = {"day1": 420, "day2": 380, "day3": 390}

myvar = pd.Series(calories, index = ["day1", "day2"])

print(myvar)

 

2. DataFrame

2.1. DataFrame

import pandas as pd

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

#load data into a DataFrame object:
df = pd.DataFrame(data)

print(df)

2.2. locate row/rows

- single bracket returns pandas series

- double bracket returns pandas dataframe

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

#load data into a DataFrame object:
df = pd.DataFrame(data)

print(df.loc[0])
print(type(df.loc[0]))
print(df.loc[[0]])
print(type(df.loc[[0]]))

 

2.3. index naming

data = {
  "calories": [420, 380, 390],
  "duration": [50, 40, 45]
}

df = pd.DataFrame(data, index = ["day1", "day2", "day3"])

print(df)

- now we can use names to locate rows

print(df.loc[['day1']])

 

3. Cleaning Data

Dataset

index,name,age,occupation,hobby,etc
0,morgan,29,student,game,coffee
1,sujan,27,doctor,hiking,
2,mark,92,,sleep,householder
3,kim,13,student,math,science
4,ihn,3s3,assa$>sin,killing,gunner
5,ahri,21,magician,flirting,fox

3.1. dropna(inplace=True)

df.dropna(inplace=True)
print(df)

3.2. fillna(value, inplace=True)

df.fillna('NULL', inplace=True)
print(df)

3.3 replace only for specific columns

df['occupation'].fillna('NULL', inplace=True)
print(df)

3.4. replacing using mean, med, mode, ...

df['age'].fillna(df['age'].mean(), inplace=True)

3.5. cleaning data

for i in df.index:
    if not df.loc[i, 'age'].isnumeric():
        ret = ''.join(c for c in df.loc[i, 'age'] if c.isnumeric())
        df.loc[i, 'age'] = ret

print(df)