pandas-code-snippets.txt

# Pandas

# Chain custom functions
result = (
    df.pipe(subtract_federal_tax)
      .pipe(subtract_state_tax, rate=0.12)
      .pipe(
          (subtract_national_insurance, 'df'),
          rate=0.05,
          rate_increase=0.02
      )
)

print(result)

# EDA

# Get info on dataset
df.info()

# List all df columns
df.columns

# Show first few rows of dataset
df.head()

# Show last few rows of dataset
df.tail()

# Number of rows in df
len(df)

# counts null values
df.isnull()

# counts NA values
df.isna()

# counts values that aren't NA
df.notna()

# assert that there are no missing values in the dataframe
assert pd.notnull(df).all().all()

# assert all values are greater than 0

assert (df >=0).all().all()

# Filters and queries 
# Show specific rows and columns
df.iloc[2:10, 5:10]

# Show specific row numbers and column names
df.loc[[3, 10, 14, 23], ["nationality", "weight_kg", "height_cm"]]

# Filter rows in df
df.query("shooting > passing")

# Show all dtypes of df columns
df.dtypes

# select the variables or columns of a certain data type
df.select_dtypes(include="int64")

# query a dataset based on a boolean condition
df["random_col"].where(df["random_col"] > 50)

# used to find out the unique values of a categorical column
df.skill_moves.unique()

# make a subset of the dataset that will contain only a few nationalities of players using .isin() function
nationality = ["Argentina", "Portugal", "Sweden", "England"]
df[df.nationality.isin(nationality)]

# provides some basic statistical measures
df["wage_eur"].describe()

# number of data in the DataFrame in the specified direction.
# When the direction is 0, it provides the number of data in the columns.
# When the direction is 1, it provides the number of data in the rows
df.count(level="Pclass")

# Totals and grouping

# provides you with the cumulative sum of all the values of the previous rows
df[["value_eur", "wage_eur"]].cumsum()

# lets you know how many unique values do you have in a column
df.nationality.nunique()

# group the data as per a certain variable
df.groupby("death")[
    "creatinine_phosphokinase",
    "ejection_fraction",
    "platelets",
    "serum_creatinine",
    "serum_sodium",
    "time",
].agg([np.mean, np.median])
.sum()

# value counts of each category
df["Pclass"].value_counts(sort=True, ascending=True, normalize=True)

pd.crosstab(
    df["league_rank"], df["international_reputation"]
)  # gives you a frequency table that is a cross-tabulation of two variables

# bins the data or segments the data based on the distribution of the data
pd.qcut(df["value_eur"], q=5)

# provide number of bins and split up dataset
pd.cut(df["value_eur"], bins=5).value_counts()

# Update dataset

# When we reset the index, the old index is added as a column, and a new sequential index is used:
df.reset_index(names=['classes', 'names'])

# Append dataframes using result
result = df1.append([df2, df3])

# Append dataframes using pd.concat
frames = [df1, df2, df3]
result = pd.concat(frames)

# Drop columns from df
df = df.drop(columns=["Unnamed: 0", "weak_foot", "real_face"])

# replaces the values of a column
df.replace(1.0, 1.1)

# rename columns
df.rename(columns={"weight_kg": "Weight (kg)", "height_cm": "Height (cm)"})

# Update segment of data
df.loc[(df['COUNCIL'] == 'LEEDS') & (df['POSTCODE'] == 'LS8'), ['CCG']] = 'LEEDS NORTH CCG'

# replaces the null values with some other value of your choice
df["pace"].fillna(0, inplace=True)

# drop null values
df.dropna()

# Drop duplicates
people = people.drop_duplicates(subset="Name")

# Drop columns from dataframe
to_drop = ['Edition Statement', 'Corporate Author', 'Corporate Contributors', 'Former owner', 'Engraver', 'Contributors',
...            'Issuance type',
...            'Shelfmarks']
>>> df.drop(to_drop, inplace=True, axis=1)

# Convert to DateTime format
pd.to_datetime(people["Graduation"])

# Change all strings to lower case in a column
people["Standing"] = people["Standing"].str.lower()

# Handling invalid values
df["height(cm)"] = pd.to_numeric(df["height(cm)"], errors='coerce')

# Split columns
df[['age','sex']] = df.age_sex.str.split("_", expand = True)

# Reorder column labels
df = df[['fname','lname','age','sex','section','height(cm)','weight(kg)','spend_A','spend_B','spend_C']]

# unpivots a DataFrame from wide format to long format
# massage a DataFrame into a format where one or more columns are identifier variables, while all other columns, considered measured variables, are unpivoted to the row axis, leaving just two non-identifier columns, variable and value.
pd.melt(df, id_vars=['A'], value_vars=['B'])

# Numpy

# Find out number of dimensions in array
df.ndim