A memorandum of method often used when analyzing data with pandas (for beginners)

Introduction

We have summarized the methods that are often used when analyzing data with pandas. We will make corrections as needed.

1. Creating a DataFrame

#Create a DataFrame and store the list
pd_sample = pd.DataFrame(list)

#Create a DataFrame and store multiple lists in a dictionary
pd_sample = pd.DataFrame({"Column A":List A, "Column B":List B})

#Create an empty DataFrame and join another DataFrame in subsequent processing
pd_sampleA = pd.DataFrame()
pd_sampleA = pd.concat([pd_sampleA, pd_sampleB])

2. Data join (union)

#pandas.concat()function
pd_sampleAll = pd.concat([pd_sampleA, pd_sampleB], ignore_index=True)

pd_sampleA

pd_sampleB

pd_sampleAll = pd.concat([pd_sampleA, pd_sampleB], ignore_index=True)

3. Join data

#pandas.merge()function
pd_sampleAll = pd.merge(pd_sampleA, pd_sampleB, on="Column label", how="left")

#pandas.DataFrame merge()Method
pd_sampleAll = pd_sampleA.merge(pd_sampleB, on="Column label", how="left")

#Combine multiple columns into a key
pd_sampleAll = pd.merge(pd_sampleA, pd_sampleB, on=["Column A", "Column B"], how="left")

#Join when key column names are different
pd_sampleAll = pd.merge(pd_sampleA, pd_sampleB, left_on="Column A", right_on="Column AAA", how="left")

pd_sampleA

pd_sampleB

** pd_sampleAll = pd.merge (pd_sampleA, pd_sampleB, on = "column A", how = "left") **

4. Data extraction

#Specify column name pandas.Extract DataFrame
pd_sampleA = pd_sampleAll[["Column A", "Column B"]]

#pandas.loc()Extract by specifying the position of row label and column label with function
#Series type is returned when a single row is fetched, and DataFrame type is returned when multiple rows are fetched.
#In addition to the line name, you can also specify a conditional expression that returns a boolean type in the line label.
#You can also slice multiple columns and rows
pd_sampleA = pd_sampleAll.loc["Row label", "Column label"]
pd_sampleA = pd_sampleAll["Column label"].loc["Row label"]

#pandas.iloc()Extract by specifying the position of row number and column number with the function
#Series type is returned when a single row is fetched, and DataFrame type is returned when multiple rows are fetched.
#You can also slice multiple columns and rows
pd_sampleA = pd_sampleAll.iloc["line number", "Column index"]

pd_sampleAll

** pd_sampleA = pd_sampleAll.loc [pd_sampleAll ["Column A"] == "aaa", ["Column B", "Column C"]] **

5. Confirmation of missing values

#pandas.Series.isnull()Get the row where the missing value exists in the method
pd_sample = pd_sample["Column A"].isnull()

#isnull()Any to method()Check for the presence of columns containing missing values using
pd_sample["Column A"].isnull().any(axis=0)

#pandas.DataFrame.dropna()Delete a row that is missing a particular column in the method
pd_sample = pd_sample.dropna(subset=["Column A"])

#pandas.DataFrame.fillna()Replace missing values with arbitrary values in the method
pd_sample = pd_sample.fillna({"Column A": "AAA", "Column B": "BBB"}))

6. Date conversion

#pandas.to_datetime()String in function(object type)To datetime type
pd_sample["date"] = pd.to_datetime(pd_sample["date"])

#Get date, hour, minute, second, day of the week, etc. using dt accessor for datetime type
pd_sample["Month"] = pd_sample["date"].dt.month

#datetime type strftime()Convert date and time to a string in any format with the method
pd_sample["Year / month"] = pd_sample["date"].dt.strftime("%Y%m")

#pandas.to_timedelta()Get the number of days difference between two dates with a function
import datetime
pd_sample["Number of days elapsed"] = pd.to_timedelta(Timestamp 1-Timestamp 2)

#Get the monthly difference between two dates using the relativedelta object of the dateutil module
#The difference between the two dates in the return value(years=+1, months=+10, days=+8)Is stored in the form of
from dateutil.relativedelta import relativedelta
delta = relativedelta(Timestamp 1,Timestamp 2)
pd_sample["Elapsed months"] = delta.years*12 + delta.months

7. Aggregation

#pandas.groupby()Aggregate by specifying the column you want to aggregate with the function and the aggregation method
#By default the group label is index, so if you don't want to do that, as_index=Specify False.
pd_count = pd_sample.groupby("The column you want to put together").count()
pd_sum = pd_sample.groupby(["Column 1 you want to summarize", "Column 2 you want to summarize"], as_index=False).sum()[["Column 1 you want to sum", "Column 2 you want to sum"]]

#pandas.unique()Get unique data count excluding duplicates with function
pd_unique = pd.unique(pd_sample["Column A"])
pd_unique = pd_sample["Column A"].unique()

8. Other methods

#pandas.agg()The function outputs the mean, median, maximum, and minimum values for a particular column
pd_sample = pd_sample.agg(["mean", "median", "max", "min"])["Columns you want to aggregate"]

#pandas.DataFrame.describe()Outputs the number of data items, mean value, standard deviation, minimum value, quartile, median value, and maximum value with the function
#By default, only numeric columns (integer type int, floating point type float) are selected
pd_sample.describe

#Manipulate strings using str accessor for DateFrame type
pd_sample["Column A"] = pd_sample["Column A"].str.replace(" ", "")

#pandas.Where in DataFrame()Assign a value according to the condition in the method
#If True, the calling object is applied, if False, the second argument is applied
pd_sample["Column A"] = pd_sample["Column A"].where(pd_sample["Column B"]>3, 0)

#pandas.DataFrame mask()Assign a value according to the condition in the method
#If True, the second argument is applied, if False, the calling object is applied.
pd_sample["Column A"] = pd_sample["Column A"].mask(pd_sample["Column B"]>3, 0)