df.to_csv(filename)# Writes to a CSV filedf.to_excel(filename)# Writes to an Excel filedf.to_sql(table_name, connection_object)# Writes to a SQL tabledf.to_json(filename)# Writes to a file in JSON formatdf.to_html(filename)# Saves as an HTML tabledf.to_clipboard()# Writes to the clipboard
Defining, then Coding and Testing Immediately
In reality, it is often more practical to define a cleaning operation, then immediately code and test it. The data wrangling template still applies here, except you'll have multiple Define, Code, and Testsubheadings, with third level headers (###) denoting each issue, as displayed below.
Saving and writing
Remember, set index=False to avoid saving with an unnamed column!
Selecting
df[col]# Returns column with label col as Seriesdf[[col1, col2]]# Returns Columns as a new DataFrames.iloc[0]# Selection by position (selects first element)s.loc[0]# Selection by index (selects element at index 0)df.iloc[0,:]# First rowdf.iloc[0,0]# First element of first column
We can select data using loc and iloc, which you can read more about here. loc uses labels of rows or columns to select data, while iloc uses the index numbers. We'll use these to index the dataframe below.
# select all the columns from 'id' to the last mean column which is called # fractal_dimension_mean (there are other cols afterthat)df_means = df.loc[:,'id':'fractal_dimension_mean']df_means.head()# Using index numbersdf_means = df.iloc[:,:12]
Another useful function that we’re going to use is pandas' query function.
In the previous lesson, we selected rows in a dataframe by indexing with a mask. Here are those same examples, along with equivalent statements that use query().
# selecting malignant records in cancer datadf_m = df[df['diagnosis']=='M']df_m = df.query('diagnosis == "M"')# selecting records of people making over $50Kdf_a = df[df['income']==' >50K']df_a = df.query('income == " >50K"')
The examples above filtered columns containing strings. You can also use query to filter columns containing numeric data like this.
# selecting records in cancer data with radius greater than the mediandf_h = df[df['radius']>13.375]df_h = df.query('radius > 13.375')
Groupby
Describing
df.shape()# Prints number of rows and columns in dataframedf.head(n)# Prints first n rows of the DataFramedf.tail(n)# Prints last n rows of the DataFramedf.info()# Index, Datatype and Memory informationdf.describe()# Summary statistics for numerical columnss.value_counts(dropna=False)# Views unique values and countsdf.apply(pd.Series.value_counts)# Unique values and counts for all columnsdf.describe()# Summary statistics for numerical columnsdf.mean()# Returns the mean of all columnsdf.corr()# Returns the correlation between columns in a DataFramedf.count()# Returns the number of non-null values in each DataFrame columndf.max()# Returns the highest value in each columndf.min()# Returns the lowest value in each columndf.median()# Returns the median of each columndf.std()# Returns the standard deviation of each column
Useful exploratory methods:
df_red.duplicated().sum()df_red.isnull().sum()
Cleaning
# Data CleaningPythondf.columns = ['a','b','c'] # Renames columnspd.isnull()# Checks for null Values, Returns Boolean Arraypd.notnull()# Opposite of s.isnull()df.dropna()# Drops all rows that contain null valuesdf.dropna(axis=1)# Drops all columns that contain null valuesdf.dropna(axis=1,thresh=n)# Drops all rows have have less than n non null valuesdf.fillna(x)# Replaces all null values with xs.fillna(s.mean()) # Replaces all null values with the mean (mean can be replaced with almost any function from the statistics section)
s.astype(float)# Converts the datatype of the series to floats.replace(1,'one')# Replaces all values equal to 1 with 'one's.replace([1,3],['one','three'])# Replaces all 1 with 'one' and 3 with 'three'df.rename(columns=lambdax: x +1)# Mass renaming of columnsdf.rename(columns =lambdax: x[:10] +"_2008", inplace=True)# Other exampledf.rename(columns={'old_name': 'new_ name'}, inplace=True)# Selective renamingdf.set_index('column_one')# Changes the indexdf.rename(index=lambdax: x +1)# Mass renaming of indexpd.to_datetimedf.drop_duplicates(inplace=True)# Example: drop columns from 2008 datasetdf_08.drop(['Stnd', 'Underhood ID', 'FE Calc Appr', 'Unadj Cmb MPG'], axis=1, inplace=True)# Example: replace spaces with underscores and lowercase labels for 2008 dataset# The strip() returns a copy of the string with both leading and trailing characters stripped.df_08.rename(columns=lambdax: x.strip().lower().replace(" ", "_"), inplace=True)# Check to see if different values are not in our columns:for phrase in asap_list:assert phrase notin df_clean.StartDate.values
# confirm column labels for 2008 and 2018 datasets are identicaldf_08.columns == df_18.columns# make sure they're all identical like this(df_08.columns == df_18.columns).all()# return duplicated rowsdf2[df2.duplicated(['user_id'], keep=False)]
Fixing data types
# convert 2018 cyl column to intdf_18['cyl']= df_18['cyl'].astype(int)
Regular expressions
# Extract int from strings in the 2008 cyl columndf_08['cyl']= df_08['cyl'].str.extract('(\d+)').astype(int)
Renaming Columns
The following helps us determine duplicated columns in different tables
Helpful script
# remove "_mean" from column namesnew_labels = []for col in df.columns:if'_mean'in col: new_labels.append(col[:-5])# exclude last 6 characterselse: new_labels.append(col)# new labels for our columnsnew_labels
Tidiness
To e deleted
for tweet in tweet_id[0:9]:
tweet = api.get_status(tweet, tweet_mode='extended')
with open('tweet_json.txt') as f:
for line in f:
status = json.loads(line)
tweet_id = # get the status ID
retweet_count = # get retweet count
favorite_count = # get favorite count
df = pd.DataFrame([[tweet_id, retweet_count, favorite_count]],
columns=['tweet_id', 'retweet_count', 'favorite_count'])
df_3 = df_3.append(df)
