Summary and Cheatsheet of Command for csv File - Python Automation and Machine Learning for ICs - - An Online Book -

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================================================================================= Table 4712a.  Data selection by label (with df.loc).  Selection Return Data Type   Example Details  Output   Single value Scalar df.loc[100, "User"]  Using scalars for both row and column selection returns a scalar  One cell value   One column (1d) Series  df.loc[:, "User"]  Shortcut: df.loc["User"]    One column (1d) Series  df.loc[[101, 102], "User"]  Using a scalar on either the row or column selection returns a Series  Two cell values   One column (2d) DataFrame  df.loc[:, ["User"]]      Multiple rows and columns DataFrame  df.loc[:, ["User", "age"]]  Selecting multiple rows and columns returns a DataFrame  Multiple rows and two columns   Range of columns DataFrame  df.loc[:, "name":"User"]       One row (1d) Series  df.loc[100, :]       One row (2d) DataFrame  df.loc[[100], :]       Multiple rows DataFrame  df.loc[[103, 100], :]       Range of rows DataFrame  df.loc[100:102, :]

Table 4712b.  Data selection by position.

Selection	Return Data Type	Example	Details	Output
Single value	Scalar	df.iloc[1, 2]	Returns a Scalar	Single cell value
One column (1d)	Series	df.iloc[:, 2]
One column (2d)	DataFrame	df.iloc[:, [2]]
Multiple rows and columns	DataFrame	df.iloc[:, [2, 1]]	Returns a DataFrame	Multiple rows and two columns
Multiple rows	Series	df.iloc[[0, 2], 1]	Returns a Series	Two cell values
Range of columns	DataFrame	df.iloc[:, :3]
One row (1d)	Series	df.iloc[1, :]
One row (2d)	DataFrame	df.iloc[[1], :]
Multiple rows	DataFrame	df.iloc[[3, 1], :]
Range of rows	DataFrame	df.iloc[1:3, :]

 Table 4712c. Data selection by boolean indexing. Boolean operators: and (&), or (|) and not (~).

Example	Details	Output
db = (df["age"] > 50) & (df["User"] == "Kevin")	Return a series with only True/False	A list of True and False
df.loc[db, :]		User ID  Name  age  User  Grade  Country  101  Kevin  51  xyz  87  USA
df.loc[df.index > 100, :]		User ID  Name  age  User  Grade  Country  101  Kevin  51  xyz  87  USA  102  Mike  13  MON  99  USA
df.loc[df["User"].isin(["xyz", "BMO"]), :]		User ID  Name  age  User  Grade  Country  101  Kevin  51  xyz  87  USA  103  Dan  20  BMO  99  USA

 Table 4712c. Data selection when DataFrames consist of only numbers.

Dataframe (df)	Python code: df < 60	Python code: df[df < 60]
Age 1  Age 2  Age 3  20  50  100  40  88  90	Age 1  Age 2  Age 3  True  True False  True  False  False	Age 1  Age 2  Age 3  20  50  NaN  40  NaN  NaN

 Table 4712d. Data joining and merging. 

Type	Description	Script	Example	Details
inner	Only rows whose (row) index exists in both DataFrames	df1.join(df2, how="inner")		Indices overlap
left	All rows from the left DataFrame, matching rows from the right DataFrame	df1.join(df2, how="left")		Indices overlap
right	All rows from the right DataFrame, matching rows from the left DataFrame	df1.join(df2, how="right")		Indices overlap
outer	The union of row indices from both DataFrames	df1.join(df2, how="outer")		Indices overlap
inner	Only rows whose (row) index exists in both DataFrames	df1.merge(df2, how="inner", on=["user"])	df1["user"] = ["a", "b", "c"] df2["user"] = ["c", "b"]	Join on one or more DF columns instead of relying on the index. merge accepts the on argument to provide one or more columns as the join condition. Output: df["user"] = ["c", "b"]
left	All rows from the left DataFrame, matching rows from the right DataFrame	df = df1.merge(df2, how="left", on=["user"])

 Table 4712e. CSV structure.

Structure	Details
Header	next() for csv.reader, df.columns

 

 Table 4712f. Functions for CSV files.

Function	Details
Save	Save images

 Table 4712g. Other applications.

Function	Details
Merge two csv files
Rename the duplicated header names in a CSV file
Change/rename a column name/header in a CSV file
Plot images from dataframe from CSVs
Read specific cells (cell by cell) in csv file
Write row-by-row into csv file

 

 

 

       

        

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