{ "source_file": "0930", "source_platform": "Medium", "author": "Federico Trotta", "title_listed": "Use Pandas Data Frames More Effectively with the Top 7 Column Operations", "keywords": "Pandas,列操作", "domain": "数据处理", "content_type": "深度技术解析", "is_paid_flag": "部分免费/会员墙", "url": "https://towardsdatascience.com/dominate-pandas-data-frames-with-the-top-7-column-operations-2a11521e9e2d/", "publish_date": "2023-07", "corpus_stage": "预训练/SFT", "notes": "", "id": "332b17fb7fd8", "collected_at": "2026-10-02T00:40:12", "title": "Use Pandas Data Frames More Effectively with the Top 7 Column Operations | Towards Data Science", "body_markdown": "![Revisit consent button](https://cdn-cookieyes.com/assets/images/revisit.svg)\nWe value your privacy\nWe use cookies to enhance your browsing experience, serve personalised ads or content, and analyse our traffic. 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It also helps in adverstising on the website.\n\n\n * Cookie\nedgebucket\n * Duration\nsession\n * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n * Cookie\ndid\n * Duration\n1 year\n * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n[Data Science](https://towardsdatascience.com/category/data-science/)\n# Use Pandas Data Frames More Effectively with the Top 7 Column Operations\nThe complete guide to performing the top 7 Pandas column operations, in different ways\n[Federico Trotta](https://towardsdatascience.com/author/federicotrotta/)\nJuly 6, 202317 min read\n![](https://assets.insightmediagroup.io/media/wp-content/uploads/2023/07/1Mbufnja0kKygoHS3LjdASg.jpeg)\nWhen it comes to data analysis, [Pandas](https://pandas.pydata.org/) is the most used [Python](https://towardsdatascience.com/how-to-study-python-for-data-science-888a1ad649ae) library to manipulate and prepare the data for further analyses and Machine Learning.\nThe reality is that Pandas is a really flexible library and [can be used even to convert file formats](https://towardsdatascience.com/how-to-convert-a-csv-file-into-an-xlsx-one-with-python-and-pandas-27aabc279d69).\nHowever, even if we use some Pandas features barely every day, we spend a lot of time Googling how to do something in Pandas.\nI know, I caught you!\nBut let's be honest: some features are difficult to remember, maybe because we can reach the same goal with different methods. So, there's nothing to be ashamed of if we Google the same things every day.\nHowever, saving time is always a good idea. For this reason, in this article, we'll go through the 7 top features to manipulate Pandas columns. This way you won't need to Google them anymore: you just need to save this article (maybe, by bookmarking it) and return to it whenever you need it.\nThis is what you'll find here:\ntext\n\n```\nTable of contents:\n\nHow to create a new Pandas column\nHow to add a new column to a Pandas data frame\nHow to rename a column in Pandas\nHow to drop a Pandas column\nHow to find unique values in a Pandas column\nHow to transform a Pandas column into a list\nHow to sort a Pandas data frame for a column\n```\n\n* * *\nLearn this step by step with the interactive [AI and Data Scientist](https://roadmap.sh/ai-data-scientist), [Computer Science](https://roadmap.sh/computer-science) and [Machine Learning](https://roadmap.sh/machine-learning) roadmaps.\n## How to create a new Pandas column\nFirst of all, let's remember that a Pandas column is also called a [Pandas series](https://pandas.pydata.org/docs/reference/api/pandas.Series.html). This means that a Pandas data frame is an ordered collection of Pandas series.\nThere are a few methods to create a new Pandas column. Let's see them all.\n### Create a Pandas column as a Pandas series\nThe correct method to create a Pandas column that is thought to \"live\" on its own is through the Pandas series method like so:\ntext\n\n```\n# Create a Panad series\nseries = pd.Series([6, 12, 18, 24])\n\n# Print Pandas series\nprint(series)\n\n>>>\n\n 0 6\n 1 12\n 2 18\n 3 24\n dtype: int64\n```\n\nI said \"the correct method\" because, as we've said, a Pandas column is a Pandas series. So, if we just need a single column we should use this method if we'd like to be \"**formally** correct\".\n### Create a Pandas column as a Pandas data frame\nHowever, the reality is that we won't need a column on its own much often.\nSo, another way to create a Pandas column is by creating a new Pandas data frame with just one column: this way, we could enrich it in a second moment with other columns.\nWe can do it like so:\ntext\n\n```\nimport pandas as pd\n\n# Create a Pandas column as a Pandas data frame\ndf = pd.DataFrame({'A': [1, 2, 3, 4]})\n\n# Print Pandas data frame\nprint(df)\n\n>>>\n\n A\n 0 1\n 1 2\n 2 3\n 3 4\n```\n\nSo, here the difference with the previous example is that, in this case, the Pandas column has also the name. In this case, we've called it \"A\".\ntext\n\n```\nNOTE:\n\nIf we take a look more closely to what we've done here, we can see that\nwe can create a Pandas data frame as a dictionary.\n\nIn fact, \"A\" is the key and it's separated by a list of values\nby a colon. Then, both the keys and the values are inside curly braces.\n```\n\n### Create a Pandas column as a Pandas data frame, starting from a NumPy array\nOne of the superpowers of Pandas is that it can \"accept\" NumPy arrays as input values. In other words, we can create a data frame starting from a NumPy array.\nIn the case of a single column, we can create a one-dimensional array and transform it into a data frame: this results in a data frame with a single column.\nWe can do it like so:\ntext\n\n```\nimport numpy as np\nimport pandas as pd\n\n# Create a NumPy array\nvalues = np.array([5, 10, 15, 20])\n\n# Transform array into Pandas data frame\ndf = pd.DataFrame(values)\n\n# Print data frame\nprint(df)\n\n>>>\n\n 0\n0 5\n1 10\n2 15\n3 20\n```\n\n* * *\n## How to add a new column to a Pandas data frame\nThe possibility to add a new column to a Pandas data frame is somehow paired with the creation of a new column.\nWhat I mean here is that we first need to create a Pandas data frame, then a single Pandas column, then we need to add the column to the data frame.\nAlso in this case we have multiple possibilities to do so. Let's see them all.\n### Adding a new column to a Pandas data frame: the standard method\nThe standard method to add a new column to a Pandas data frame is to create the data frame, then create a separate column, then add it to the data frame.\nWe'll use this method throughout all the following examples. So, here's how we can do so:\ntext\n\n```\nimport pandas as pd\n\n# Create a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3, 4]})\n\n# Add a new column by using a list of values\ndf['B'] = [20, 30, 40, 50]\n\n# Print data frame\nprint(df)\n\n>>>\n\n A B\n0 1 20\n1 2 30\n2 3 40\n3 4 50\n```\n\nSo, let's analyze what we've done step by step:\n 1. We've created a Pandas data frame with the method `pd.DataFrame()`.\n 2. We've created a new column with `df['B']`, meaning we called this new column \"B\".\n 3. We've assigned the values to the newly created column, with a list of numbers.\n\n\nSo, what's another method to create a new column? Is by using **a list of numbers** , if we already have a data frame.\n### Adding a new column to a Pandas data frame: applying functions\nThe power of the standard method to add a new column to an existing data frame gives us the possibility to create a new column and add it to an existing data frame, all in one line of code.\nFor example, say that we want to create two new columns as a combination of an existing column. We can do so by applying functions to existing columns like so:\ntext\n\n```\nimport pandas as pd\n\n# Create a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3, 4]})\n\n# Create a column doubling the values of column A\ndf['B'] = df['A'] * 2\n\n# Apply lambda function to column A to create column C\ndf['C'] = df['A'].apply(lambda x: x ** 2)\n\n# Print data frame\nprint(df)\n\n>>>\n\n A B C\n0 1 2 1\n1 2 4 4\n2 3 6 9\n3 4 8 16\n```\n\nSo, here's what we've done:\n 1. We've created a Pandas column (\"A\") as a data frame.\n 2. We've created column \"B\" by doubling the values of column \"A\".\n 3. We've created column \"C\" by applying a lambda function to column \"A\". In particular, in this case, we're squaring the values of column \"A\".\n\n\nAll of these columns are stored together in a unique data frame.\n### Adding a new column to a Pandas data frame: using Pandas series or single Pandas columns\nOf course, we can add columns to a Pandas data frame even when the columns are Pandas series or Pandas data frame.\nHere's how we can do so:\ntext\n\n```\nimport pandas as pd\n\n# Create a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3, 4]})\n\n# Create a new column using pd.Series()\nvalues = pd.Series([5, 10, 15, 20]) # Create series\ndf['B'] = values # Add series to data frame as a column\n\n# Print data frame\nprint(df)\n\n>>>\n\n A B\n0 1 5\n1 2 10\n2 3 15\n3 4 20\n```\n\nSo, in the above case, we've created a Pandas series and, then, we've added it to the existing data frame by giving it a name.\nIn the case of a Pandas column created as a Pandas data frame, we have:\ntext\n\n```\nimport pandas as pd\n\n# Create a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3, 4], 'B': [5, 6, 7, 8]})\n\n# Create a Pandas column as a data frame\ndf['C'] = pd.DataFrame({'C': [9, 10, 11, 12]})\n\n# Print data frame\nprint(df)\n\n>>>\n\n A B C\n0 1 5 9\n1 2 6 10\n2 3 7 11\n3 4 8 12\n```\n\nAnd here we are.\ntext\n\n```\nNOTE:\n\nof course, the same methodology can be applied if we create a column\nas a NumPy array. We won't show the method here as \"the game\" should\nnow be clear.\n```\n\n* * *\n## How to rename a column in Pandas\nRenaming a Pandas column (or more than one) is another typical daily task we need to perform, but that we often can't remember.\nAlso, in this case, we have different methods to do so. Let's see them all.\n### How to rename a Pandas column: the rename() method\nWe can rename a Pandas column with the `rename()` method like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n\n# Renaming a single column\ndf = df.rename(columns={'A': 'NewA'})\n\n# Print data frame\nprint(df)\n\n>>>\n NewA B\n0 1 4\n1 2 5\n2 3 6\n\n```\n\nSo, it's like we're using a dictionary. Inside the `rename()` method, in fact, we need to pass the argument `columns` and specify the actual name and the new name inside curly braces, separating them with a column. Just like we do in dictionaries.\nOf course, we can use this method to rename multiple columns like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n\n# Rename multiple columns\ndf = df.rename(columns={'A': 'NewA', 'B': 'NewB'})\n\n# Print data frame\nprint(df)\n\n>>>\n\n NewA NewB\n0 1 4\n1 2 5\n2 3 6\n```\n\nAnd, again, it's as we'd work with dictionaries.\n### How to rename a Pandas column: the column attribute\nTo rename one Pandas column (or more than one, as we'll see) we can use the `columns` attribute like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n\n# Renaming all columns\ndf.columns = ['NewA', 'NewB']\n\n# Print data frame\nprint(df)\n\n>>>\n\n NewA NewB\n0 1 4\n1 2 5\n2 3 6\n```\n\nSo, in this case, the `columns` attribute gives us the possibility to use a list of strings to rename the columns.\n### How to rename a Pandas column: the set_axis() method\nTo rename one (or more than one) Pandas column we can use the `set_axis()` method like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6]})\n\n# Renaming all columns\ndf.set_axis(['NewA', 'NewB'], axis=1, inplace=True)\n\n# Print data frame\nprint(df)\n\n>>>\n\n NewA NewB\n0 1 4\n1 2 5\n2 3 6\n```\n\nSo, even in this case, we use a list of strings to rename the columns, but here we also need to pass the parameters `axis=1` and `inplace=True` because the `set_axis()` method sets the axes from zero, so is recreating them. This lets this method rename the columns.\n### How to rename a Pandas column: using lambda functions\nWhen we have to deal with strings as in the case of Pandas columns names, we can use lambda functions to modify the characters of the text.\nFor example, we may want (or need) to rename the columns by simply lowering the letters. We can do it like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'COLUMN_1': [1, 2, 3], 'COLUMN_2': [4, 5, 6]})\n\n# Renaming columns using a lambda function\ndf = df.rename(columns=lambda x: x.lower()) # Lowercase column names\n\n# Print data frame\nprint(df)\n\n>>>\n\n column_1 column_2\n0 1 4\n1 2 5\n2 3 6\n```\n\nAnd here we are.\n* * *\n## How to drop a Pandas column\nDropping a Pandas column (or more than one) is another task we need to perform very often. Maybe because its values are not significant, maybe because its values are all `NULL` or for other reasons.\nTo perform this task we have two methods. Let's see them both.\n### How to drop a Pandas column: using the drop() method\nThe typical method to drop a Pandas column (or more than one) is by using the `drop()` method.\nHere the only thing to get in mind is to decide if we want to drop some columns and create a new data frame, or if we want to drop them and substitute the current data frame.\nLet me show the difference:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9]})\n\n# Drop one column and substitute the current data frame\ndf = df.drop('A', axis=1)\n\n# Print updated data frame\nprint(df)\n\n>>>\n\n B C\n0 4 7\n1 5 8\n2 6 9\n```\n\nSo, we've dropped the column \"A\" by using the `drop()` method specifying the name of the column we wanted to drop and the axis (`axis=1` in Pandas indicates the vertical direction and must be specified).\nIn this case, we've decided to substitute the data frame `df`. So, at the end of the process, the data frame `df` hasn't column \"A\".\nInstead, if we want to create another data frame, let's say we call id `df_2`, we have to do it like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9],\n 'D': [10, 11, 12]})\n\n# Drop one column and substitute the current data frame\ndf_2 = df.drop(['A', 'D'], axis=1)\n\n# Print new data frame\nprint(df_2)\n\n>>>\n\n B C\n0 4 7\n1 5 8\n2 6 9\n```\n\nSo, in this case, we've dropped two columns and created a new data frame with just columns \"B\" and \"C\".\nThis may be useful if we think we may need the original data frame `df` in the future, for further analyses.\n### How to drop a Pandas column: using the column index\nIn Pandas, columns can be singled out via the indexes. This means that we can drop them using indexes like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3], 'B': [4, 5, 6], 'C': [7, 8, 9],\n 'D': [10, 11, 12]})\n\n# Drop one column and append to a new data frame\ndf_2 = df.drop(df.columns[[0, 1]], axis=1)\n\n# Print new data frame\nprint(df_2)\n\n>>>\n\n C D\n0 7 10\n1 8 11\n2 9 12\n```\n\nSo, in this case, we've created a new data frame with just columns \"C\" and \"D\" and we've deleted columns \"A\" and \"B\" by using their indexes.\nRemembering that in Python we start counting from 0 (so, the first column is at index 0 and is column \"A\"), we have to say that this method may not be optimal if we have tens of columns for a simple reason: we should find the one (or the ones) we want to drop by counting them, which is subject to errors.\n* * *\n## How to find unique values in a Pandas column\nFinding unique values in a Pandas column is another task that we may need to perform daily, because duplicated values have to be treated in a particular way.\nAlso in this case we have a couple of methods to do so: one shows the duplicates in one column and the other removes them.\nLet's see them both.\n### How to find unique values in a Pandas column: using the value_counts() method to find duplicates\nIf we want to see if a Pandas column has duplicated values, and we also want to see how much are they, we can use the `value_counts()` like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3, 1, 3], 'B': [4, 5, 6, 7, 8,],\n 'C': [7, 8, 9, 10, 11]})\n\n# Find unique values in a Pandas column\nunique_values = df['A'].value_counts()\n\n# Print unique values\nprint(unique_values)\n\n>>>\n\n1 2\n3 2\n2 1\nName: A, dtype: int64\n```\n\nSo, the result here tells that:\n 1. The name of the column is \"A\" and the types are all \"int64\".\n 2. We have two 1s.\n 3. We have two 3s.\n 4. We have one 2.\n\n\nSo, it shows us the values and tells us how many of them are present in the column of our interest.\n### How to find unique values in a Pandas column: using the drop_duplicates() method to drop duplicates\nIf we want to drop the duplicates values in a Pandas column (because we know there are duplicates in it) we can use the `drop_duplicates()` method like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3, 1, 3], 'B': [4, 5, 6, 7, 8,],\n 'C': [7, 8, 9, 10, 11]})\n\n# Drop the duplicate values in a Pandas column\nunique_values = df['A'].drop_duplicates()\n\n# Print unique values\nprint(unique_values)\n\n>>>\n\n0 1\n1 2\n2 3\n```\n\nSo, we have removed the duplicates from column \"A\", creating a new Pandas column called `unique_values`.\n### How to find unique values in a Pandas column: studying a data frame\nAt this point, you may be asking:\" _Well, if I have a big data frame with tens of columns, how can I know that properly some columns have some duplicates_?\"\nGood question! The thing we can do is to first study the whole data frame.\nFor example, we may want to see if any columns have duplicates. We can do it like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame with duplicates\ndf = pd.DataFrame({'A': [1, 2, 2, 3, 4, 4], 'B': [5, 6, 6, 7, 8, 8]})\n\n# Check if there are duplicates in the DataFrame\nhas_duplicates = df.duplicated().any()\n\n# Print the result\nprint(has_duplicates)\n\n>>>\n\nTrue\n```\n\nSo, this code returns \"True\" if there are columns with duplicates and \"False\" if there aren't.\nAnd how about if we want to know the name of the columns that actually have duplicates? We can do it like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame with duplicates\ndf = pd.DataFrame({'A': [1, 2, 2, 3, 4, 4], 'B': [5, 6, 6, 7, 8, 8]})\n\n# Find duplicate rows\nduplicate_rows = df.duplicated()\n\n# Print the duplicate rows\nprint(df[duplicate_rows])\n\n>>>\n\n A B\n2 2 6\n5 4 8\n```\n\nAnd, so, the above code shows:\n * The columns with the duplicates.\n * The values of the duplicates.\n\n\nWe can now investigate any further with the `value_counts()` method or drop them with the `drop_duplicates()` method.\n* * *\n## How to transform a Pandas column into a list\nTransforming a Pandas column into a list is a useful feature that can give us the possibility to \"isolate\" all the values from a Pandas column to put them into a list. Then, we can do whatever we may need with a list, which is easily manageable (iterating, and so on).\nWe have two possibilities to do this transformation.\n### How to transform a Pandas column into a list: using the list() method\nThe `list()` method is a built-in Python function that converts an iterable object into a list. We can use it like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3, 1, 3], 'B': [4, 5, 6, 7, 8,],\n 'C': [7, 8, 9, 10, 11]})\n\n# Transform Pandas column into a list\ncolumn_list = list(df['B'])\n\n# Print list\nprint(column_list)\n\n>>>\n\n[4, 5, 6, 7, 8]\n```\n\nSo, we've easily extracted our values and put them into a list.\n### How to transform a Pandas column into a list: using the to_list() method\nTo achieve the same result, we can use the `to_list()` method from Pandas. But take care: **it's available from Pandas version 1.2.0 or later**.\nWe can use it like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [1, 2, 3, 1, 3], 'B': [4, 5, 6, 7, 8,],\n 'C': [7, 8, 9, 10, 11]})\n\n# Transform Pandas column into a list\ncolumn_list = df['B'].to_list()\n\n# Print list\nprint(column_list)\n\n>>>\n\n[4, 5, 6, 7, 8]\n```\n\nAnd, of course, we've obtained the same result as before.\n* * *\n## How to sort a Pandas data frame for a column\nThere are a lot of situations in which we need to sort our columns. By sorting we mean ordering, so we can choose to order the data in ascending or descending way.\nWe can reach this goal with the following methods.\n### How to sort a Pandas data frame for a column: using the sort_values() method\nTo sort a Pandas data frame for a column, we can use the `sort_values()`like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [10, 2, 7, 1, 15], 'B': [4, 2, 6, 28, 8,],\n 'C': [7, 1, 9, 10, 19]})\n\n# Sort df for A in ascending order\ndf.sort_values('A', ascending=True, inplace=True)\n\n# Print sorted data frame\nprint(df)\n\n>>>\n\n A B C\n3 1 28 10\n1 2 2 1\n2 7 6 9\n0 10 4 7\n4 15 8 19\n```\n\nSo, as we can see, the data frame has been sorted with column \"A\" in ascending order. In fact, if we check:\n * In the initial data frame, in column \"A\" the number 1 is in the 4th position. In column \"B\", the number 28 is in the fourth position.\n * In the sorted data frame, in column \"A\", the number 1 is in the first position. In column \"B\", the number 28 is in the first position.\n\n\nSo, we sort the data frame but we don't lose the relations between the values of the columns.\nA very useful feature of this method is that it can sort a column by putting `NaNs` as first values. We can do it like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [10, 0, 0, 1, 15], 'B': [4, 2, 6, 28, 8,],\n 'C': [7, 1, 15, 10, 19]})\n\n# Sort NaNs in the beginning\ndf.sort_values('A', ascending=True, inplace=True)\n\n# Print sorted data frame\nprint(df)\n\n>>>\n\n A B C\n1 0 2 1\n2 0 6 15\n3 1 28 10\n0 10 4 7\n4 15 8 19\n```\n\nAnd here we are.\n### How to sort a Pandas data frame for a column: using the sort_index() method\nWe can also sort a data frame for index value like so:\ntext\n\n```\nimport pandas as pd\n\n# Creating a DataFrame\ndf = pd.DataFrame({'A': [10, 2, 7, 1, 15], 'B': [4, 2, 6, 28, 8,],\n 'C': [7, 1, 9, 10, 19]})\n\n# Sort data frame for index\ndf.sort_index(inplace=True)\n\n# Print sorted data frame\nprint(df)\n\n>>>\n\n A B C\n0 10 4 7\n1 2 2 1\n2 7 6 9\n3 1 28 10\n4 15 8 19\n```\n\nAnd, as we can see, the indexes are ordered (in ascending way).\n* * *\n## Conclusions\nIn this article, we've seen the top 7 operations on Pandas columns that we perform barely every day.\nThis guide will help you save a lot of time if you save it, because we've performed the same task in different ways so that you won't need to Google them anymore, saving a lot of time.\n* * *\nHi, I'm Federico Trotta and I'm a freelance Technical Writer.\nWant to collaborate with me? 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