Pandas Common Functions
Pandas provides a large number of functions for data processing and analysis. The following are some commonly used functions:
General Functions
| Function | Description |
|---|---|
pd.isna(obj) | Check whether the object is a missing value. |
pd.notna(obj) | Check whether the object is not a missing value. |
pd.concat(objs, axis) | Concatenate multiple objects. |
pd.merge(left, right, on) | Merge DataFrames by columns. |
pd.get_dummies(data) | One-Hot encode categorical variables. |
pd.cut(x, bins) | Bin continuous data. |
pd.qcut(x, q) | Bin by quantiles. |
pd.to_numeric(arg) | Convert to numeric. |
pd.to_datetime(arg) | Convert to datetime. |
pd.unique(values) | Get unique values. |
pd.value_counts(values) | Count frequencies. |
pd.factorize(values) | Encode categorical variables. |
pd.crosstab(index, columns) | Cross tabulation. |
pd.pivot_table(data) | Pivot table. |
pd.melt(frame) | Wide to long. |
Data Reading and Writing (IO)
| Function | Description |
|---|---|
pd.read_csv() | Read CSV file. |
pd.read_excel() | Read Excel. |
pd.read_json() | Read JSON. |
pd.read_html() | Parse HTML tables. |
pd.read_sql() | Read from database. |
df.to_csv() | Write CSV. |
df.to_excel() | Write Excel. |
df.to_json() | Write JSON. |
df.to_parquet() | Write Parquet. |
Data Cleaning
| Function | Description |
|---|---|
df.dropna() | Drop missing values. |
df.fillna() | Fill missing values. |
df.replace() | Replace data. |
df.drop_duplicates() | Remove duplicates. |
df.astype() | Type conversion. |
df.rename() | Rename columns. |
df.sort_values() | Sort. |
df.reset_index() | Reset index. |
Data Selection and Filtering
| Function | Description |
|---|---|
df.head() | First few rows. |
df.tail() | Last few rows. |
df.loc[] | Label-based indexing. |
df.iloc[] | Position-based indexing. |
df.query() | Conditional filtering. |
df.filter() | Column filtering. |
Grouping and Aggregation
| Function | Description |
|---|---|
df.groupby() | Grouping operation. |
groupby.sum() | Aggregate sum. |
groupby.mean() | Mean. |
groupby.agg() | Multiple aggregations. |
groupby.transform() | Transform. |
Mathematical and Statistical Functions
| Function | Description |
|---|---|
Series.sum() | Sum. |
Series.mean() | Mean. |
Series.median() | Median. |
Series.std() | Standard deviation. |
Series.var() | Variance. |
Series.corr() | Correlation coefficient. |
Series.quantile() | Quantile. |
Series.cumsum() | Cumulative sum. |
String Processing
| Function | Description |
|---|---|
Series.str.lower() | Lowercase. |
Series.str.upper() | Uppercase. |
Series.str.strip() | Strip whitespace. |
Series.str.replace() | Replace. |
Series.str.contains() | Match. |
Series.str.split() | Split. |
Series.str.len() | Length. |
Time Series
| Function | Description |
|---|---|
pd.date_range() | Generate dates. |
pd.Timestamp() | Timestamp. |
pd.Timedelta() | Time difference. |
Series.dt.year | Year. |
Series.dt.month | Month. |
Series.dt.day | Day. |
Series.dt.weekday | Weekday. |
Data Reshaping
| Function | Description |
|---|---|
df.pivot() | Pivot. |
df.pivot_table() | Pivot table. |
df.stack() | Columns to rows. |
df.unstack() | Rows to columns. |
pd.melt() | Wide to long. |
Example
import pandas as pd
# General Functions
s = pd.Series([1, 2, 3, None])
print(pd.isna(s))
# Math
print(s.sum())
# String
s_str = pd.Series(['a', 'b'])
print(s_str.str.upper())
# Time
dates = pd.to_datetime(['2023-01-01'])
print(dates.dt.month)
# General Functions
s = pd.Series([1, 2, 3, None])
print(pd.isna(s))
# Math
print(s.sum())
# String
s_str = pd.Series(['a', 'b'])
print(s_str.str.upper())
# Time
dates = pd.to_datetime(['2023-01-01'])
print(dates.dt.month)
If you need more detailed information, you can refer toPandas Official Documentation。
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