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chevron_left Creating DataFrames Cookbook
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chevron_left Creating DataFrames Cookbook
Combining multiple Series into a DataFrameCombining multiple Series to form a DataFrameConverting a Series to a DataFrameConverting list of lists into DataFrameConverting list to DataFrameConverting percent string into a numeric for read_csvConverting scikit-learn dataset to Pandas DataFrameConverting string data into a DataFrameCreating a DataFrame from a stringCreating a DataFrame using listsCreating a DataFrame with different type for each columnCreating a DataFrame with empty valuesCreating a DataFrame with missing valuesCreating a DataFrame with random numbersCreating a DataFrame with zerosCreating a MultiIndex DataFrameCreating a Pandas DataFrameCreating a single DataFrame from multiple filesCreating empty DataFrame with only column labelsFilling missing values when using read_csvImporting DatasetImporting tables from PostgreSQL as Pandas DataFramesInitialising a DataFrame using a constantInitialising a DataFrame using a dictionaryInitialising a DataFrame using a list of dictionariesInserting lists into a DataFrame cellKeeping leading zeroes when using read_csvParsing dates when using read_csvPreventing strings from getting parsed as NaN for read_csvReading data from GitHubReading file without headerReading large CSV files in chunksReading n random lines using read_csvReading space-delimited filesReading specific columns from fileReading tab-delimited filesReading the first few lines of a file to create DataFrameReading the last n lines of a fileReading URL using read_csvReading zipped csv file as a DataFrameRemoving Unnamed:0 columnResolving ParserError: Error tokenizing dataSaving DataFrame as zipped csvSkipping rows without skipping header for read_csvSpecifying data type for read_csvTreating missing values as empty strings rather than NaN for read_csv
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Parsing dates when using read_csv in Pandas

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Creating DataFrames Cookbook
schedule Jul 1, 2022
Last updated
local_offer PythonPandas
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Parsing columns as datetime

Consider the following my_data.txt file:

A,B
2020/12/25,7
2020/12,8
2020,9

To parse column A as a datetime when using read_csv(~):

df = pd.read_csv("my_data.txt", parse_dates=["A"])
A datetime64[ns]
B int64
dtype: object

Parsing index as datetime

Consider the following my_data.txt file:

A
2020/12/25,7
2020/12,8
2020,9

To parse the index as datetime:

df = pd.read_csv("my_data.txt", parse_dates=True)
df
A
2020-12-25 7
2020-12-01 8
2020-01-01 9

Here, the index is of type DatetimeIndex:

DatetimeIndex(['2020-12-25', '2020-12-01', '2020-01-01'], dtype='datetime64[ns]', freq=None)

Combining multiple columns to form a single datetime column

Consider the following my_data.txt file:

Year,Month
2020,7
2020,8
2020,9

Using a nested list

To combine columns Year and Month to form a single datetime column:

df = pd.read_csv("my_data.txt", parse_dates=[["Year","Month"]])
df
Year_Month
0 2020-07-01
1 2020-08-01
2 2020-09-01

To confirm its data type:

Year_Month datetime64[ns]
dtype: object

Using a dictionary

To combine columns Year and Month to form a single datetime column:

df = pd.read_csv("my_data.txt", parse_dates={"A":["Year","Month"]})
df
A
0 2020-07-01
1 2020-08-01
2 2020-09-01

Using a dictionary is more flexible than using a nested list because:

  • you can specify a label to the combined column (e.g. "A" in this case)

  • you can specify multiple groups of columns to combine as a single date column.

robocat
Published by Isshin Inada
Edited by 0 others
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