You're now ready to import the CSV file into Python using read_csv() from pandas: import pandas as pdĬereal_df = pd.read_csv("/tmp/tmp07wuam09/data/cereal.csv")Ĭereal_df2 = pd.read_csv("data/cereal.csv") Now that you know what your current working directory is and where the dataset is in your filesystem, you can specify the file path to it. Print(pd.DataFrame.equals(cereal_df, cereal_df2))Īs you can see in the code chunk above, the file path is the main argument to read_csv() and it was specified in two ways. You can use the full file path which is prefixed by a / and includes the working directory in the specification, or use the relative file path which doesn't. The read_csv() function is smart enough to decipher whether it's working with full or relative file paths and convert your flat file as a DataFrame without a problem. ( Note: the environment for every DataCamp session is temporary, so the working directory you saw in the previous section may not be identical to the one you see in the code chunk above.)Ĭontinue on and see how else pandas makes importing CSV files easier. Let's use some of the function's customizable options, particularly for the way it deals with headers, incorrect data types, and missing data. Headers refer to the column names of your dataset. If you’re using SQL Developer, there may be a time where you want to import a CSV file into your Oracle database.For some datasets you might encounter, the headers may be completely missing, partially missing, or they might exist, but you may want to rename them. SQL Developer includes a wizard that lets you import a file. Here’s the sample CSV we’ll use in this article: created_date,product_name,category_id,price We’ll load a CSV (Comma Separated Values) file into our Oracle database using SQL Developer. If you want to follow along, you can download this CSV file here: sample_csv_data.csv. Step 2: In the Connections panel, you have two methods, depending on whether you have a table already: Step 1: Open SQL Developer and connect to your database. If you already have a table to import data into, right-click on the table and select Import Data. If you don’t have a table, you can create one as part of the import process. ![]() Right-click on the Tables entry and select Import Data. In the steps below, we’ll assume that a table does not exist, and we’ve selected Import Data by right-clicking on the Tables item. You’ll see the Data Import Wizard screen. ![]() Step 4: Change any of the settings you need for your file if needed. You can see what your table would look like with some of the rows from your file in a preview at the bottom. In the Preview, we can see that the product_name column data has single quotes. We don’t want the single quotes as part of the data, so we can specify that these are our “left enclosure” and “right enclosure” characters.Ĭhange these values from double quotes to single quotes. We can see the quotes no longer appear in the data. Step 5: Click Next to go to the Import Method screen. Step 6: Enter a table name to create for the data you are importing. In this example, we’ve entered a table name of “new_products”. When we do this, we notice that a new step in the process appears: Choose Columns. Step 7: Leave the defaults and click Next. You can change a couple of things on this screen if you want to.Insert: this will create and execute an INSERT statement for each row in the table.This means the data is inserted into your table.Insert Script: this will create an SQL script with a range of Insert statements without running them.This can be helpful if you want to save the file or modify it.External Table: this creates an External Table object for the data to be stored.Staging External Table: this also creates an external table, but can be used as a staging table for inserting into a target table.SQL*Loader Utility: this uses the SQL*Loader feature.You can also set the Import Row Limit, which is the maximum number of rows that will be imported. Step 8: On the Choose Columns screen, select any columns you do not want to import and move them to the Available Columns section. This will be useful if your CSV file has more columns than your destination table. ![]() You can decide which columns to import and which to ignore.īy default, all columns will be imported. If this is what you want, proceed to the next step. Step 10: On the Column Definition screen, select the data type and format for each of the columns you are importing. SQL Developer makes a pretty good guess based on the data in each column.
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