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Python Personal Index
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PYTHON PERSONAL INDEX

Introduction Python

  • Python as a calculator

# Addition, subtraction
print(5 + 5)
print(5 - 5)

# Multiplication, division, modulo, and exponentiation
print(3 * 5)
print(10 / 2)
print(18 % 7)
print(4 ** 2)

# How much is your $100 worth after 7 years?
print(100*1.1**7)

  • Functions:

print()
type()
int()
float()
bool()
len()
help()
max()
pow()
sorted(): sorted(, reverse=)

  • Methods

x.upper()
x.count()
x.index()
x.remove()
x.reverse()

Data Manipulation with Pandas

.head(): returns the first few rows (the “head” of the DataFrame).
.info(): shows information on each of the columns, such as the data type and number of missing values.
.shape: returns the number of rows and columns of the DataFrame.
.describe(): calculates a few summary statistics for each column.
.values: A two-dimensional NumPy array of values.
.columns: An index of columns: the column names.
.index: An index for the rows: either row numbers or row names.
.sort_values()
  • df.sort_values(""): one column
  • df.sort_values(["", ""]):multiple column
Subsetting columns
  • df["col_a"]
  • df[["col_a", "col_b"]]
Subsetting rows
  • df[df["x"] > 60]
  • df[df["y"] == "tan"]
  • df[(df["x"] > 60) & (df["y"] == "tan")]
Subsetting rows by categorical variables
  • df[(df["col"] == "value_1") | (df["col"] == "value_2")]
  • df[df["col"].isin(list)]
Summarizing numerical data
  • median() , .mode()
  • .min() , .max()
  • .var() , .std()
  • .sum()
  • .quantile()
Aggregations
  • def function(column):
  • return column.quantile(0.3)
  • df['column'].agg(function)
Cumulative statistics
  • .cummax()
  • .cummin()
  • .cumprod()